Preview features used by data frame analytics
Previews the extracted features used by a data frame analytics config.
Body
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config object
Hide config attributes Show config attributes object
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Hide source attributes Show source attributes object
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query object
Hide query attributes Show query attributes object
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bool object
Hide bool attributes Show bool attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
filter object | array[object]
The clause (query) must appear in matching documents. However, unlike
must
, the score of the query will be ignored.minimum_should_match number | string
The minimum number of terms that should match as integer, percentage or range
must object | array[object]
The clause (query) must appear in matching documents and will contribute to the score.
must_not object | array[object]
The clause (query) must not appear in the matching documents. Because scoring is ignored, a score of
0
is returned for all documents.should object | array[object]
The clause (query) should appear in the matching document.
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boosting object
Hide boosting attributes Show boosting attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
Floating point number between 0 and 1.0 used to decrease the relevance scores of documents matching the
negative
query.
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combined_fields object
Hide combined_fields attributes Show combined_fields attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
Text to search for in the provided
fields
. Thecombined_fields
query analyzes the provided text before performing a search. -
If true, match phrase queries are automatically created for multi-term synonyms.
-
operator string
Values are
or
orand
. minimum_should_match number | string
The minimum number of terms that should match as integer, percentage or range
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zero_terms_query string
Values are
none
orall
.
-
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constant_score object
Hide constant_score attributes Show constant_score attributes object
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dis_max object
Hide dis_max attributes Show dis_max attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
One or more query clauses. Returned documents must match one or more of these queries. If a document matches multiple queries, Elasticsearch uses the highest relevance score.
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tie_breaker number
Floating point number between 0 and 1.0 used to increase the relevance scores of documents matching multiple query clauses.
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exists object
Hide exists attributes Show exists attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
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function_score object
Hide function_score attributes Show function_score attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
boost_mode string
Values are
multiply
,replace
,sum
,avg
,max
, ormin
. -
functions array[object]
One or more functions that compute a new score for each document returned by the query.
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max_boost number
Restricts the new score to not exceed the provided limit.
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min_score number
Excludes documents that do not meet the provided score threshold.
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query object
-
score_mode string
Values are
multiply
,sum
,avg
,first
,max
, ormin
.
-
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fuzzy object
Returns documents that contain terms similar to the search term, as measured by a Levenshtein edit distance.
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geo_bounding_box object
Hide geo_bounding_box attributes Show geo_bounding_box attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
type string
Values are
memory
orindexed
. -
validation_method string
Values are
coerce
,ignore_malformed
, orstrict
. -
ignore_unmapped boolean
Set to
true
to ignore an unmapped field and not match any documents for this query. Set tofalse
to throw an exception if the field is not mapped.
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geo_distance object
Hide geo_distance attributes Show geo_distance attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
distance_type string
Values are
arc
orplane
. -
validation_method string
Values are
coerce
,ignore_malformed
, orstrict
. -
ignore_unmapped boolean
Set to
true
to ignore an unmapped field and not match any documents for this query. Set tofalse
to throw an exception if the field is not mapped.
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-
geo_polygon object
Hide geo_polygon attributes Show geo_polygon attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
validation_method string
Values are
coerce
,ignore_malformed
, orstrict
. -
ignore_unmapped boolean
-
-
geo_shape object
Hide geo_shape attributes Show geo_shape attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
ignore_unmapped boolean
Set to
true
to ignore an unmapped field and not match any documents for this query. Set tofalse
to throw an exception if the field is not mapped.
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has_child object
Hide has_child attributes Show has_child attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
ignore_unmapped boolean
Indicates whether to ignore an unmapped
type
and not return any documents instead of an error. -
inner_hits object
Hide inner_hits attributes Show inner_hits attributes object
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name string
-
size number
The maximum number of hits to return per
inner_hits
. -
from number
Inner hit starting document offset.
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collapse object
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docvalue_fields array[object]
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explain boolean
-
ignore_unmapped boolean
-
seq_no_primary_term boolean
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fields string | array[string]
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stored_fields string | array[string]
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track_scores boolean
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version boolean
-
-
max_children number
Maximum number of child documents that match the query allowed for a returned parent document. If the parent document exceeds this limit, it is excluded from the search results.
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min_children number
Minimum number of child documents that match the query required to match the query for a returned parent document. If the parent document does not meet this limit, it is excluded from the search results.
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score_mode string
Values are
none
,avg
,sum
,max
, ormin
.
-
-
has_parent object
Hide has_parent attributes Show has_parent attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
ignore_unmapped boolean
Indicates whether to ignore an unmapped
parent_type
and not return any documents instead of an error. You can use this parameter to query multiple indices that may not contain theparent_type
. -
inner_hits object
Hide inner_hits attributes Show inner_hits attributes object
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name string
-
size number
The maximum number of hits to return per
inner_hits
. -
from number
Inner hit starting document offset.
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collapse object
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docvalue_fields array[object]
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explain boolean
-
ignore_unmapped boolean
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seq_no_primary_term boolean
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fields string | array[string]
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stored_fields string | array[string]
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track_scores boolean
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version boolean
-
-
score boolean
Indicates whether the relevance score of a matching parent document is aggregated into its child documents.
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ids object
Hide ids attributes Show ids attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
values string | array[string]
-
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intervals object
Returns documents based on the order and proximity of matching terms.
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knn object
Hide knn attributes Show knn attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
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query_vector array[number]
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query_vector_builder object
Hide query_vector_builder attribute Show query_vector_builder attribute object
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text_embedding object
-
-
num_candidates number
The number of nearest neighbor candidates to consider per shard
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k number
The final number of nearest neighbors to return as top hits
filter object | array[object]
Filters for the kNN search query
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similarity number
The minimum similarity for a vector to be considered a match
-
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match object
Returns documents that match a provided text, number, date or boolean value. The provided text is analyzed before matching.
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match_all object
Hide match_all attributes Show match_all attributes object
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match_bool_prefix object
Analyzes its input and constructs a
bool
query from the terms. Each term except the last is used in aterm
query. The last term is used in a prefix query. -
match_none object
Hide match_none attributes Show match_none attributes object
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match_phrase object
Analyzes the text and creates a phrase query out of the analyzed text.
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match_phrase_prefix object
Returns documents that contain the words of a provided text, in the same order as provided. The last term of the provided text is treated as a prefix, matching any words that begin with that term.
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more_like_this object
Hide more_like_this attributes Show more_like_this attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
analyzer string
The analyzer that is used to analyze the free form text. Defaults to the analyzer associated with the first field in fields.
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boost_terms number
Each term in the formed query could be further boosted by their tf-idf score. This sets the boost factor to use when using this feature. Defaults to deactivated (0).
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fail_on_unsupported_field boolean
Controls whether the query should fail (throw an exception) if any of the specified fields are not of the supported types (
text
orkeyword
). -
fields array[string]
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
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include boolean
Specifies whether the input documents should also be included in the search results returned.
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max_doc_freq number
The maximum document frequency above which the terms are ignored from the input document.
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max_query_terms number
The maximum number of query terms that can be selected.
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max_word_length number
The maximum word length above which the terms are ignored. Defaults to unbounded (
0
). -
min_doc_freq number
The minimum document frequency below which the terms are ignored from the input document.
minimum_should_match number | string
The minimum number of terms that should match as integer, percentage or range
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min_term_freq number
The minimum term frequency below which the terms are ignored from the input document.
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min_word_length number
The minimum word length below which the terms are ignored.
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routing string
stop_words string | array[string]
Language value, such as arabic or thai. Defaults to english. Each language value corresponds to a predefined list of stop words in Lucene. See Stop words by language for supported language values and their stop words. Also accepts an array of stop words.
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unlike array[string | object]
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version number
-
version_type string
Values are
internal
,external
,external_gte
, orforce
.
-
-
multi_match object
Hide multi_match attributes Show multi_match attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
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_name string
-
analyzer string
Analyzer used to convert the text in the query value into tokens.
-
If
true
, match phrase queries are automatically created for multi-term synonyms. -
fields string | array[string]
fuzziness string | number
-
fuzzy_rewrite string
-
fuzzy_transpositions boolean
If
true
, edits for fuzzy matching include transpositions of two adjacent characters (for example,ab
toba
). Can be applied to the term subqueries constructed for all terms but the final term. -
lenient boolean
If
true
, format-based errors, such as providing a text query value for a numeric field, are ignored. -
max_expansions number
Maximum number of terms to which the query will expand.
minimum_should_match number | string
The minimum number of terms that should match as integer, percentage or range
-
operator string
Values are
and
,AND
,or
, orOR
. -
prefix_length number
Number of beginning characters left unchanged for fuzzy matching.
-
Text, number, boolean value or date you wish to find in the provided field.
-
slop number
Maximum number of positions allowed between matching tokens.
-
tie_breaker number
Determines how scores for each per-term blended query and scores across groups are combined.
-
type string
Values are
best_fields
,most_fields
,cross_fields
,phrase
,phrase_prefix
, orbool_prefix
. -
zero_terms_query string
Values are
all
ornone
.
-
-
nested object
Hide nested attributes Show nested attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
ignore_unmapped boolean
Indicates whether to ignore an unmapped path and not return any documents instead of an error.
-
inner_hits object
Hide inner_hits attributes Show inner_hits attributes object
-
name string
-
size number
The maximum number of hits to return per
inner_hits
. -
from number
Inner hit starting document offset.
-
collapse object
-
docvalue_fields array[object]
-
explain boolean
-
ignore_unmapped boolean
-
seq_no_primary_term boolean
-
fields string | array[string]
-
stored_fields string | array[string]
-
track_scores boolean
-
version boolean
-
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
score_mode string
Values are
none
,avg
,sum
,max
, ormin
.
-
-
parent_id object
Hide parent_id attributes Show parent_id attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
id string
-
ignore_unmapped boolean
Indicates whether to ignore an unmapped
type
and not return any documents instead of an error. -
type string
-
-
percolate object
Hide percolate attributes Show percolate attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
document object
The source of the document being percolated.
-
documents array[object]
An array of sources of the documents being percolated.
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
id string
-
index string
-
name string
The suffix used for the
_percolator_document_slot
field when multiplepercolate
queries are specified. -
preference string
Preference used to fetch document to percolate.
-
routing string
-
version number
-
-
pinned object
Hide pinned attributes Show pinned attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
ids array[string]
Document IDs listed in the order they are to appear in results. Required if
docs
is not specified. -
docs array[object]
Documents listed in the order they are to appear in results. Required if
ids
is not specified.
-
-
prefix object
Returns documents that contain a specific prefix in a provided field.
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query_string object
Hide query_string attributes Show query_string attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
allow_leading_wildcard boolean
If
true
, the wildcard characters*
and?
are allowed as the first character of the query string. -
analyzer string
Analyzer used to convert text in the query string into tokens.
-
analyze_wildcard boolean
If
true
, the query attempts to analyze wildcard terms in the query string. -
If
true
, match phrase queries are automatically created for multi-term synonyms. -
default_field string
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
default_operator string
Values are
and
,AND
,or
, orOR
. -
enable_position_increments boolean
If
true
, enable position increments in queries constructed from aquery_string
search. -
escape boolean
-
fields array[string]
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
fuzziness string | number
-
fuzzy_max_expansions number
Maximum number of terms to which the query expands for fuzzy matching.
-
fuzzy_prefix_length number
Number of beginning characters left unchanged for fuzzy matching.
-
fuzzy_rewrite string
-
fuzzy_transpositions boolean
If
true
, edits for fuzzy matching include transpositions of two adjacent characters (for example,ab
toba
). -
lenient boolean
If
true
, format-based errors, such as providing a text value for a numeric field, are ignored. -
max_determinized_states number
Maximum number of automaton states required for the query.
minimum_should_match number | string
The minimum number of terms that should match as integer, percentage or range
-
phrase_slop number
Maximum number of positions allowed between matching tokens for phrases.
-
Query string you wish to parse and use for search.
-
quote_analyzer string
Analyzer used to convert quoted text in the query string into tokens. For quoted text, this parameter overrides the analyzer specified in the
analyzer
parameter. -
quote_field_suffix string
Suffix appended to quoted text in the query string. You can use this suffix to use a different analysis method for exact matches.
-
rewrite string
-
tie_breaker number
How to combine the queries generated from the individual search terms in the resulting
dis_max
query. -
time_zone string
-
type string
Values are
best_fields
,most_fields
,cross_fields
,phrase
,phrase_prefix
, orbool_prefix
.
-
-
range object
Returns documents that contain terms within a provided range.
-
rank_feature object
Hide rank_feature attributes Show rank_feature attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
saturation object
-
log object
-
linear object
-
sigmoid object
-
-
regexp object
Returns documents that contain terms matching a regular expression.
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rule object
Hide rule attributes Show rule attributes object
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script object
Hide script attributes Show script attributes object
-
script_score object
Hide script_score attributes Show script_score attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
min_score number
Documents with a score lower than this floating point number are excluded from the search results.
-
-
semantic object
Hide semantic attributes Show semantic attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
The field to query, which must be a semantic_text field type
-
The query text
-
-
shape object
Hide shape attributes Show shape attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
ignore_unmapped boolean
When set to
true
the query ignores an unmapped field and will not match any documents.
-
-
simple_query_string object
Hide simple_query_string attributes Show simple_query_string attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
analyzer string
Analyzer used to convert text in the query string into tokens.
-
analyze_wildcard boolean
If
true
, the query attempts to analyze wildcard terms in the query string. -
If
true
, the parser creates a match_phrase query for each multi-position token. -
default_operator string
Values are
and
,AND
,or
, orOR
. -
fields array[string]
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
fuzzy_max_expansions number
Maximum number of terms to which the query expands for fuzzy matching.
-
fuzzy_prefix_length number
Number of beginning characters left unchanged for fuzzy matching.
-
fuzzy_transpositions boolean
If
true
, edits for fuzzy matching include transpositions of two adjacent characters (for example,ab
toba
). -
lenient boolean
If
true
, format-based errors, such as providing a text value for a numeric field, are ignored. minimum_should_match number | string
The minimum number of terms that should match as integer, percentage or range
-
Query string in the simple query string syntax you wish to parse and use for search.
-
quote_field_suffix string
Suffix appended to quoted text in the query string.
-
-
span_containing object
Hide span_containing attributes Show span_containing attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Hide big attributes Show big attributes object
-
Hide little attributes Show little attributes object
-
-
span_field_masking object
Hide span_field_masking attributes Show span_field_masking attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
Hide query attributes Show query attributes object
-
-
span_first object
Hide span_first attributes Show span_first attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Controls the maximum end position permitted in a match.
-
Hide match attributes Show match attributes object
-
-
span_multi object
Hide span_multi attributes Show span_multi attributes object
-
span_near object
Hide span_near attributes Show span_near attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Array of one or more other span type queries.
-
in_order boolean
Controls whether matches are required to be in-order.
-
slop number
Controls the maximum number of intervening unmatched positions permitted.
-
-
span_not object
Hide span_not attributes Show span_not attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
dist number
The number of tokens from within the include span that can’t have overlap with the exclude span. Equivalent to setting both
pre
andpost
. -
Hide exclude attributes Show exclude attributes object
-
Hide include attributes Show include attributes object
-
post number
The number of tokens after the include span that can’t have overlap with the exclude span.
-
pre number
The number of tokens before the include span that can’t have overlap with the exclude span.
-
-
span_or object
Hide span_or attributes Show span_or attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Array of one or more other span type queries.
-
-
span_term object
Matches spans containing a term.
-
span_within object
Hide span_within attributes Show span_within attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Hide big attributes Show big attributes object
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Hide little attributes Show little attributes object
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sparse_vector object
Hide sparse_vector attributes Show sparse_vector attributes object
-
boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
query string
The query text you want to use for search. If inference_id is specified, query must also be specified.
-
prune boolean
Whether to perform pruning, omitting the non-significant tokens from the query to improve query performance. If prune is true but the pruning_config is not specified, pruning will occur but default values will be used. Default: false
-
pruning_config object
-
query_vector object
Dictionary of precomputed sparse vectors and their associated weights. Only one of inference_id or query_vector may be supplied in a request.
-
inference_id string
-
-
term object
Returns documents that contain an exact term in a provided field. To return a document, the query term must exactly match the queried field's value, including whitespace and capitalization.
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terms object
Hide terms attributes Show terms attributes object
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terms_set object
Returns documents that contain a minimum number of exact terms in a provided field. To return a document, a required number of terms must exactly match the field values, including whitespace and capitalization.
-
Uses a natural language processing model to convert the query text into a list of token-weight pairs which are then used in a query against a sparse vector or rank features field.
-
Supports returning text_expansion query results by sending in precomputed tokens with the query.
-
wildcard object
Returns documents that contain terms matching a wildcard pattern.
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wrapper object
Hide wrapper attributes Show wrapper attributes object
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boost number
Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
-
_name string
-
A base64 encoded query. The binary data format can be any of JSON, YAML, CBOR or SMILE encodings
-
-
type object
Hide type attributes Show type attributes object
-
-
runtime_mappings object
Hide runtime_mappings attributes Show runtime_mappings attributes object
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fields object
Hide fields attributes Show fields attributes object
-
Values are
boolean
,composite
,date
,double
,geo_point
,ip
,keyword
,long
, orlookup
.
-
fetch_fields array[object]
For type
lookup
-
format string
A custom format for
date
type runtime fields. -
input_field string
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
target_field string
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
target_index string
-
script object
Hide script attributes Show script attributes object
-
source string
The script source.
-
id string
-
params object
Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.
Hide params attributes Show params attributes object
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key object
-
options object
Hide options attributes Show options attributes object
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key string
-
-
Values are
boolean
,composite
,date
,double
,geo_point
,ip
,keyword
,long
, orlookup
.
-
_source object
Hide _source attributes Show _source attributes object
-
An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
-
An array of strings that defines the fields that will be included in the analysis.
-
-
Hide analysis attributes Show analysis attributes object
-
classification object
Hide classification attributes Show classification attributes object
-
alpha number
Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.
-
Defines which field of the document is to be predicted. It must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable. For classification analysis, the data type of the field must be numeric (
integer
,short
,long
,byte
), categorical (ip
orkeyword
), orboolean
. There must be no more than 30 different values in this field. For regression analysis, the data type of the field must be numeric. -
downsample_factor number
Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.
-
early_stopping_enabled boolean
Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable.
-
eta number
Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
-
eta_growth_rate_per_tree number
Advanced configuration option. Specifies the rate at which
eta
increases for each new tree that is added to the forest. For example, a rate of 1.05 increaseseta
by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2. -
feature_bag_fraction number
Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.
-
feature_processors array[object]
Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple
feature_processors
entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields.Hide feature_processors attributes Show feature_processors attributes object
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frequency_encoding object
-
multi_encoding object
-
n_gram_encoding object
-
one_hot_encoding object
-
target_mean_encoding object
-
-
gamma number
Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
-
lambda number
Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
-
Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.
-
max_trees number
Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.
-
Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
-
prediction_field_name string
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
randomize_seed number
Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as
source
andanalyzed_fields
are the same). -
soft_tree_depth_limit number
Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the
soft_tree_depth_tolerance
to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0. -
soft_tree_depth_tolerance number
Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds
soft_tree_depth_limit
. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01. training_percent string | number
-
class_assignment_objective string
-
num_top_classes number
Defines the number of categories for which the predicted probabilities are reported. It must be non-negative or -1. If it is -1 or greater than the total number of categories, probabilities are reported for all categories; if you have a large number of categories, there could be a significant effect on the size of your destination index. NOTE: To use the AUC ROC evaluation method,
num_top_classes
must be set to -1 or a value greater than or equal to the total number of categories.
-
-
outlier_detection object
Hide outlier_detection attributes Show outlier_detection attributes object
-
compute_feature_influence boolean
Specifies whether the feature influence calculation is enabled.
-
feature_influence_threshold number
The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1.
-
method string
The method that outlier detection uses. Available methods are
lof
,ldof
,distance_kth_nn
,distance_knn
, andensemble
. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score. -
n_neighbors number
Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.
-
outlier_fraction number
The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.
-
standardization_enabled boolean
If true, the following operation is performed on the columns before computing outlier scores:
(x_i - mean(x_i)) / sd(x_i)
.
-
-
regression object
Hide regression attributes Show regression attributes object
-
alpha number
Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.
-
Defines which field of the document is to be predicted. It must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable. For classification analysis, the data type of the field must be numeric (
integer
,short
,long
,byte
), categorical (ip
orkeyword
), orboolean
. There must be no more than 30 different values in this field. For regression analysis, the data type of the field must be numeric. -
downsample_factor number
Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.
-
early_stopping_enabled boolean
Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable.
-
eta number
Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
-
eta_growth_rate_per_tree number
Advanced configuration option. Specifies the rate at which
eta
increases for each new tree that is added to the forest. For example, a rate of 1.05 increaseseta
by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2. -
feature_bag_fraction number
Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.
-
feature_processors array[object]
Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple
feature_processors
entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields.Hide feature_processors attributes Show feature_processors attributes object
-
frequency_encoding object
-
multi_encoding object
-
n_gram_encoding object
-
one_hot_encoding object
-
target_mean_encoding object
-
-
gamma number
Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
-
lambda number
Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
-
Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.
-
max_trees number
Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.
-
Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
-
prediction_field_name string
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
-
randomize_seed number
Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as
source
andanalyzed_fields
are the same). -
soft_tree_depth_limit number
Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the
soft_tree_depth_tolerance
to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0. -
soft_tree_depth_tolerance number
Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds
soft_tree_depth_limit
. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01. training_percent string | number
-
loss_function string
The loss function used during regression. Available options are
mse
(mean squared error),msle
(mean squared logarithmic error),huber
(Pseudo-Huber loss). -
loss_function_parameter number
A positive number that is used as a parameter to the
loss_function
.
-
-
-
model_memory_limit string
-
max_num_threads number
-
analyzed_fields object
Hide analyzed_fields attributes Show analyzed_fields attributes object
-
An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
-
An array of strings that defines the fields that will be included in the analysis.
-
-
Responses
-
200 application/json
Hide response attribute Show response attribute object
-
An array of objects that contain feature name and value pairs. The features have been processed and indicate what will be sent to the model for training.
Hide feature_values attributes Show feature_values attributes object
-
key string
-
curl \
-X POST http://api.example.com/_ml/data_frame/analytics/_preview \
-H "Content-Type: application/json" \
-d '{"config":{"source":{"index":"string","query":{"":{"boost":42.0,"_name":"string","value":"string"},"common":{},"fuzzy":{},"intervals":{},"match":{},"match_bool_prefix":{},"match_phrase":{},"match_phrase_prefix":{},"prefix":{},"range":{},"regexp":{},"span_term":{},"term":{},"terms_set":{},"text_expansion":{},"weighted_tokens":{},"wildcard":{}},"":{"fields":{"type":"boolean"},"fetch_fields":[{"field":"string","format":"string"}],"format":"string","input_field":"string","target_field":"string","target_index":"string","script":{"source":"string","id":"string","params":{"key":{}},"":"painless","options":{"key":"string"}},"type":"boolean"},"_source":{"includes":["string"],"excludes":["string"]}},"analysis":{"":{"alpha":42.0,"dependent_variable":"string","downsample_factor":42.0,"early_stopping_enabled":true,"eta":42.0,"eta_growth_rate_per_tree":42.0,"feature_bag_fraction":42.0,"feature_processors":[{"frequency_encoding":{},"multi_encoding":{},"n_gram_encoding":{},"one_hot_encoding":{},"target_mean_encoding":{}}],"gamma":42.0,"lambda":42.0,"max_optimization_rounds_per_hyperparameter":42.0,"max_trees":42.0,"num_top_feature_importance_values":42.0,"prediction_field_name":"string","randomize_seed":42.0,"soft_tree_depth_limit":42.0,"soft_tree_depth_tolerance":42.0,"":"string","loss_function":"string","loss_function_parameter":42.0},"outlier_detection":{"compute_feature_influence":true,"feature_influence_threshold":42.0,"method":"string","n_neighbors":42.0,"outlier_fraction":42.0,"standardization_enabled":true}},"model_memory_limit":"string","max_num_threads":42.0,"analyzed_fields":{"includes":["string"],"excludes":["string"]}}}'