Logistic Regression

Logistic regression. Currently, this class only supports binary classification.

Input

This takes in a DataFrame and performs Logistic Regression

Output

The Logistic Regression Model generated is passed along to the next nodes. The input DataFrame is also passed along to the next nodes

Type

ml-estimator

Class

fire.nodes.ml.NodeLogisticRegression

Fields

Name	Title	Description
featuresCol	Features Column	Features column of type vectorUDT for model fitting
labelCol	Label Column	The label column for model fitting
predictionCol	Prediction Column	The prediction column created during model scoring
splitRatio	Split Ratio	Split Ratio
maxIter	Maximum Iterations	Maximum number of iterations (>= 0)
fitIntercept	Fit Intercept	Whether to fit an intercept term
regParam	Regularization Param	The regularization parameter
standardization	Standardization	Whether to standardize the training features before fitting the model
threshold	Threshold	The threshold in binary classification prediction
tol	Tolerance	The convergence tolerance for iterative algorithms
elasticNetParam	ElasticNet Param	The ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty
weightCol	Weight Column	If the ‘weight column’ is not specified, all instances are treated equally with a weight 1.0
aggregationDepth	Aggregation Depth	Param for suggested depth for treeAggregate (>= 2)
family	Family	Param for the name of family which is a description of the label distribution to be used in the model
gridSearch	Grid Search
regParamGrid	Regularization Param Grid Search	Regularization Parameters for Grid Search
elasticNetGrid	ElasticNet Param Grid Search	ElasticNet Parameters for Grid Search
confusionMatrix	Confusion Matrix
output_confusion_matrix_chart	Output Confusion Matrix Chart	whether to display confusion matrix chart.
cm_chart_title	Confusion Matrix Chart Title	Title name to display in Confusion Matrix Chart
cm_chart_description	Confusion Matrix Chart Description	Description to display in Confusion Matrix CHart
confusionMatrixTargetLegend	Confusion Matrix Target Legend	Legend name to display for Target in Confusion Matrix
confusionMatrixPredictedLabelLegend	Confusion Matrix PredictedLabel Legend	Legend name to display for Predicted Label in Confusion Matrix
confusionMatrixCountLegend	Confusion Matrix Count Legend	Legend name to display for Count in Confusion Matrix
confusionMatrixCountLegend	Confusion Matrix Count Legend	Legend name to display for Count in Confusion Matrix
Description	Confusion Matrix Description
confusionMatrixRowDescription	Confusion Matrix Outcome description	One can provide the business details of the outcome of the confusion matrix rows
ROC Curve	ROC Curve
output_roc_curve	Output ROC Curve	whether to display confusion matrix chart.
roc_title	ROC Curve Chart Title	Title name to display in ROC Curve Chart
roc_description	ROC Curve Chart Description	Add Description for ROC Curve Chart
xlabel	X Label	X label
ylabel	Y Label	Y Label

Details

Logistic regression is a popular method to predict a categorical response.

It is a special case of Generalized Linear models that predicts the probability of the outcomes.

In spark.ml logistic regression can be used to predict a binary outcome by using binomial logistic regression, or it can be used to predict a multiclass outcome by using multinomial logistic regression.

More details are available at:

http://spark.apache.org/docs/latest/ml-classification-regression.html#logistic-regression

Examples

The below example is available at : https://spark.apache.org/docs/2.3.0/ml-classification-regression.html#logistic-regression

import org.apache.spark.ml.classification.LogisticRegression

// Load training data

val training = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

val lr = new LogisticRegression()

.setMaxIter(10)

.setRegParam(0.3)

.setElasticNetParam(0.8)

// Fit the model

val lrModel = lr.fit(training)

// Print the coefficients and intercept for logistic regression

println(s”Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}”)

// We can also use the multinomial family for binary classification

val mlr = new LogisticRegression()

.setMaxIter(10)

.setRegParam(0.3)

.setElasticNetParam(0.8)

.setFamily(“multinomial”)

val mlrModel = mlr.fit(training)

// Print the coefficients and intercepts for logistic regression with multinomial family

println(s”Multinomial coefficients: ${mlrModel.coefficientMatrix}”)

println(s”Multinomial intercepts: ${mlrModel.interceptVector}”)