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Merge remote-tracking branch 'es/master' into enrich
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Martijn van Groningen
commit
e06598ba56
412 changed files with 6674 additions and 4217 deletions
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@ -20,14 +20,52 @@ include-tagged::{doc-tests-file}[{api}-request]
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<1> Constructing a new evaluation request
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<2> Reference to an existing index
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<3> The query with which to select data from indices
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<4> Kind of evaluation to perform
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<5> Name of the field in the index. Its value denotes the actual (i.e. ground truth) label for an example. Must be either true or false
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<6> Name of the field in the index. Its value denotes the probability (as per some ML algorithm) of the example being classified as positive
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<7> The remaining parameters are the metrics to be calculated based on the two fields described above.
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<8> https://en.wikipedia.org/wiki/Precision_and_recall[Precision] calculated at thresholds: 0.4, 0.5 and 0.6
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<9> https://en.wikipedia.org/wiki/Precision_and_recall[Recall] calculated at thresholds: 0.5 and 0.7
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<10> https://en.wikipedia.org/wiki/Confusion_matrix[Confusion matrix] calculated at threshold 0.5
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<11> https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated and the curve points returned
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<4> Evaluation to be performed
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==== Evaluation
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Evaluation to be performed.
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Currently, supported evaluations include: +BinarySoftClassification+, +Classification+, +Regression+.
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===== Binary soft classification
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-evaluation-softclassification]
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--------------------------------------------------
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<1> Constructing a new evaluation
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<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) label for an example. Must be either true or false.
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<3> Name of the field in the index. Its value denotes the probability (as per some ML algorithm) of the example being classified as positive.
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<4> The remaining parameters are the metrics to be calculated based on the two fields described above
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<5> https://en.wikipedia.org/wiki/Precision_and_recall#Precision[Precision] calculated at thresholds: 0.4, 0.5 and 0.6
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<6> https://en.wikipedia.org/wiki/Precision_and_recall#Recall[Recall] calculated at thresholds: 0.5 and 0.7
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<7> https://en.wikipedia.org/wiki/Confusion_matrix[Confusion matrix] calculated at threshold 0.5
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<8> https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated and the curve points returned
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===== Classification
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-evaluation-classification]
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--------------------------------------------------
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<1> Constructing a new evaluation
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<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) class the example belongs to.
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<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) class of the example.
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<4> The remaining parameters are the metrics to be calculated based on the two fields described above
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<5> Multiclass confusion matrix of size 3
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===== Regression
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-evaluation-regression]
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--------------------------------------------------
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<1> Constructing a new evaluation
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<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) value for an example.
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<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) value for the example.
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<4> The remaining parameters are the metrics to be calculated based on the two fields described above
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<5> https://en.wikipedia.org/wiki/Mean_squared_error[Mean squared error]
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<6> https://en.wikipedia.org/wiki/Coefficient_of_determination[R squared]
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include::../execution.asciidoc[]
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@ -41,7 +79,40 @@ The returned +{response}+ contains the requested evaluation metrics.
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include-tagged::{doc-tests-file}[{api}-response]
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--------------------------------------------------
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<1> Fetching all the calculated metrics results
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<2> Fetching precision metric by name
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<3> Fetching precision at a given (0.4) threshold
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<4> Fetching confusion matrix metric by name
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<5> Fetching confusion matrix at a given (0.5) threshold
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==== Results
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===== Binary soft classification
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-results-softclassification]
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--------------------------------------------------
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<1> Fetching precision metric by name
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<2> Fetching precision at a given (0.4) threshold
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<3> Fetching confusion matrix metric by name
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<4> Fetching confusion matrix at a given (0.5) threshold
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===== Classification
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-results-classification]
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--------------------------------------------------
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<1> Fetching multiclass confusion matrix metric by name
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<2> Fetching the contents of the confusion matrix
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<3> Fetching the number of classes that were not included in the matrix
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===== Regression
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-results-regression]
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--------------------------------------------------
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<1> Fetching mean squared error metric by name
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<2> Fetching the actual mean squared error value
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<3> Fetching R squared metric by name
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<4> Fetching the actual R squared value
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@ -76,7 +76,7 @@ include-tagged::{doc-tests-file}[{api}-dest-config]
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==== Analysis
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The analysis to be performed.
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Currently, the supported analyses include : +OutlierDetection+, +Regression+.
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Currently, the supported analyses include: +OutlierDetection+, +Classification+, +Regression+.
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===== Outlier detection
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@ -101,6 +101,24 @@ include-tagged::{doc-tests-file}[{api}-outlier-detection-customized]
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<6> The proportion of the data set that is assumed to be outlying prior to outlier detection
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<7> Whether to apply standardization to feature values
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===== Classification
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+Classification+ analysis requires to set which is the +dependent_variable+ and
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has a number of other optional parameters:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-classification]
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--------------------------------------------------
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<1> Constructing a new Classification builder object with the required dependent variable
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<2> The lambda regularization parameter. A non-negative double.
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<3> The gamma regularization parameter. A non-negative double.
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<4> The applied shrinkage. A double in [0.001, 1].
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<5> The maximum number of trees the forest is allowed to contain. An integer in [1, 2000].
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<6> The fraction of features which will be used when selecting a random bag for each candidate split. A double in (0, 1].
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<7> The name of the prediction field in the results object.
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<8> The percentage of training-eligible rows to be used in training. Defaults to 100%.
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===== Regression
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+Regression+ analysis requires to set which is the +dependent_variable+ and
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@ -12,8 +12,8 @@ API Key can be created using this API.
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[id="{upid}-{api}-request"]
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==== Create API Key Request
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A +{request}+ contains name for the API key,
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list of role descriptors to define permissions and
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A +{request}+ contains an optional name for the API key,
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an optional list of role descriptors to define permissions and
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optional expiration for the generated API key.
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If expiration is not provided then by default the API
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keys do not expire.
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@ -37,4 +37,4 @@ expiration.
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include-tagged::{doc-tests-file}[{api}-response]
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--------------------------------------------------
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<1> the API key that can be used to authenticate to Elasticsearch.
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<2> expiration if the API keys expire
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<2> expiration if the API keys expire
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