JSDoc: Class: LogisticRegression

Name	Type	Attributes	Description
`dataset`	module:eclairjs/sql.DataFrame
`paramMap`	module:eclairjs/ml/param.ParamMap	<optional>	Parameter map. These values override any specified in this Estimator's embedded ParamMap.

Overrides:

module:eclairjs/ml/classification.ProbabilisticClassifier#fit

Source:

ml/classification/LogisticRegression.js, line 329

Returns:

fitted model

Type: module:eclairjs/ml/classification.LogisticRegressionModel

getThreshold() → {Promise.<float>}

Source:

ml/classification/LogisticRegression.js, line 218

Returns:

Type: Promise.<float>

getThresholds() → {Promise.<Array.<float>>}

Source:

ml/classification/LogisticRegression.js, line 281

Returns:

Type: Promise.<Array.<float>>

maxIter() → {module:eclairjs/ml/param.Param}

Source:

ml/classification/LogisticRegression.js, line 130

Returns:

Type: module:eclairjs/ml/param.Param

probabilityCol() → {module:eclairjs/ml/param.Param}

Inherited From:

module:eclairjs/ml/classification.ProbabilisticClassifier#probabilityCol

Source:

ml/classification/ProbabilisticClassifier.js, line 58

Returns:

Type: module:eclairjs/ml/param.Param

regParam() → {module:eclairjs/ml/param.Param}

Source:

ml/classification/LogisticRegression.js, line 78

Returns:

Type: module:eclairjs/ml/param.Param

setElasticNetParam(value) → {module:eclairjs/ml/classification.LogisticRegression}

Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

Parameters:

Name	Type	Description
`value`	float

Source:

ml/classification/LogisticRegression.js, line 99

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setFeaturesCol(value) → {module:eclairjs/ml.Predictor}

Parameters:

Name	Type	Description
`value`	string

Inherited From:

module:eclairjs/ml.Predictor#setFeaturesCol

Source:

ml/Predictor.js, line 61

Returns:

Type: module:eclairjs/ml.Predictor

setFitIntercept(value) → {module:eclairjs/ml/classification.LogisticRegression}

Whether to fit an intercept term. Default is true.

Parameters:

Name	Type	Description
`value`	boolean

Source:

ml/classification/LogisticRegression.js, line 168

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setLabelCol(value) → {module:eclairjs/ml.Predictor}

Parameters:

Name	Type	Description
`value`	string

Inherited From:

module:eclairjs/ml.Predictor#setLabelCol

Source:

ml/Predictor.js, line 46

Returns:

Type: module:eclairjs/ml.Predictor

setMaxIter(value) → {module:eclairjs/ml/classification.LogisticRegression}

Set the maximum number of iterations. Default is 100.

Parameters:

Name	Type	Description
`value`	integer

Source:

ml/classification/LogisticRegression.js, line 116

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setPredictionCol(value) → {module:eclairjs/ml.Predictor}

Parameters:

Name	Type	Description
`value`	string

Inherited From:

module:eclairjs/ml.Predictor#setPredictionCol

Source:

ml/Predictor.js, line 76

Returns:

Type: module:eclairjs/ml.Predictor

setProbabilityCol(value) → {object}

Parameters:

Name	Type	Description
`value`	string

Inherited From:

module:eclairjs/ml/classification.ProbabilisticClassifier#setProbabilityCol

Source:

ml/classification/ProbabilisticClassifier.js, line 44

Returns:

Type: object

setRawPredictionCol(value) → {module:eclairjs/ml/classification.Classifier}

Parameters:

Name	Type	Description
`value`	string

Inherited From:

module:eclairjs/ml/classification.Classifier#setRawPredictionCol

Source:

ml/classification/Classifier.js, line 45

Returns:

Type: module:eclairjs/ml/classification.Classifier

setRegParam(value) → {module:eclairjs/ml/classification.LogisticRegression}

Set the regularization parameter. Default is 0.0.

Parameters:

Name	Type	Description
`value`	float

Source:

ml/classification/LogisticRegression.js, line 64

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setStandardization(value) → {module:eclairjs/ml/classification.LogisticRegression}

Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

Parameters:

Name	Type	Description
`value`	boolean

Source:

ml/classification/LogisticRegression.js, line 189

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setThreshold(value) → {module:eclairjs/ml/classification.LogisticRegression}

Parameters:

Name	Type	Description
`value`	float

Source:

ml/classification/LogisticRegression.js, line 204

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setThresholds(value) → {module:eclairjs/ml/classification.LogisticRegression}

Parameters:

Name	Type	Description
`value`	Array.<float>

Overrides:

module:eclairjs/ml/classification.ProbabilisticClassifier#setThresholds

Source:

ml/classification/LogisticRegression.js, line 267

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setTol(value) → {module:eclairjs/ml/classification.LogisticRegression}

Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

Parameters:

Name	Type	Description
`value`	float

Source:

ml/classification/LogisticRegression.js, line 150

Returns:

Type: module:eclairjs/ml/classification.LogisticRegression

setWeightCol(value) → {module:eclairjs/ml/classification.LogisticRegression}

Whether to over-/under-sample training instances according to the given weights in weightCol. If not set or empty String, all instances are treated equally (weight 1.0). Default is not set, so all instances have weight one.