LogisticRegression

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

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 : https://spark.apache.org/docs/2.3.0/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}”)