ml-classification-regression.md

layout: global
title: Classification and regression - spark.ml
displayTitle: Classification and regression in spark.ml

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Table of Contents

• This will become a table of contents (this text will be scraped). {:toc}

In MLlib, we implement popular linear methods such as logistic regression and linear least squares with L_1 or L_2 regularization. Refer to the linear methods in mllib for details. In spark.ml, we also include Pipelines API for Elastic net, a hybrid of L_1 and L_2 regularization proposed in Zou et al, Regularization and variable selection via the elastic net. Mathematically, it is defined as a convex combination of the L_1 and the L_2 regularization terms: \[ \alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0 \] By setting \alpha properly, elastic net contains both L_1 and L_2 regularization as special cases. For example, if a linear regression model is trained with the elastic net parameter \alpha set to 1, it is equivalent to a Lasso model. On the other hand, if \alpha is set to 0, the trained model reduces to a ridge regression model. We implement Pipelines API for both linear regression and logistic regression with elastic net regularization.

Classification

Logistic regression

Logistic regression is a popular method to predict a binary response. It is a special case of Generalized Linear models that predicts the probability of the outcome. For more background and more details about the implementation, refer to the documentation of the logistic regression in spark.mllib.

The current implementation of logistic regression in spark.ml only supports binary classes. Support for multiclass regression will be added in the future.

Example

The following example shows how to train a logistic regression model with elastic net regularization. elasticNetParam corresponds to \alpha and regParam corresponds to \lambda.

{% include_example scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala %}

{% include_example java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java %}

{% include_example python/ml/logistic_regression_with_elastic_net.py %}

The spark.ml implementation of logistic regression also supports extracting a summary of the model over the training set. Note that the predictions and metrics which are stored as Dataframe in BinaryLogisticRegressionSummary are annotated @transient and hence only available on the driver.

LogisticRegressionTrainingSummary provides a summary for a LogisticRegressionModel. Currently, only binary classification is supported and the summary must be explicitly cast to BinaryLogisticRegressionTrainingSummary. This will likely change when multiclass classification is supported.

Continuing the earlier example:

{% include_example scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala %}

LogisticRegressionTrainingSummary provides a summary for a LogisticRegressionModel. Currently, only binary classification is supported and the summary must be explicitly cast to BinaryLogisticRegressionTrainingSummary. This will likely change when multiclass classification is supported.

Continuing the earlier example:

{% include_example java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java %}

Logistic regression model summary is not yet supported in Python.

Decision tree classifier

Decision trees are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on decision trees.

Example

The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the DataFrame which the Decision Tree algorithm can recognize.

More details on parameters can be found in the Scala API documentation.

{% include_example scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala %}

More details on parameters can be found in the Java API documentation.

{% include_example java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java %}

More details on parameters can be found in the Python API documentation.

{% include_example python/ml/decision_tree_classification_example.py %}

Random forest classifier

Random forests are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on random forests.

Example

The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the DataFrame which the tree-based algorithms can recognize.

Refer to the Scala API docs for more details.

{% include_example scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala %}

Refer to the Java API docs for more details.

{% include_example java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java %}

Refer to the Python API docs for more details.

{% include_example python/ml/random_forest_classifier_example.py %}

Gradient-boosted tree classifier

Gradient-boosted trees (GBTs) are a popular classification and regression method using ensembles of decision trees. More information about the spark.ml implementation can be found further in the section on GBTs.

Example

The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the DataFrame which the tree-based algorithms can recognize.

Refer to the Scala API docs for more details.

{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala %}

Refer to the Java API docs for more details.

{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java %}

Refer to the Python API docs for more details.

{% include_example python/ml/gradient_boosted_tree_classifier_example.py %}

Multilayer perceptron classifier

Multilayer perceptron classifier (MLPC) is a classifier based on the feedforward artificial neural network. MLPC consists of multiple layers of nodes. Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes maps inputs to the outputs by performing linear combination of the inputs with the node's weights $\wv$ and bias $\bv$ and applying an activation function. It can be written in matrix form for MLPC with $K+1$ layers as follows: \[ \mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K) \] Nodes in intermediate layers use sigmoid (logistic) function: \[ \mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}} \] Nodes in the output layer use softmax function: \[ \mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}} \] The number of nodes $N$ in the output layer corresponds to the number of classes.

MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.

Example

{% include_example scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala %}

{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %}

{% include_example python/ml/multilayer_perceptron_classification.py %}

One-vs-Rest classifier (a.k.a. One-vs-All)

OneVsRest is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently. It is also known as "One-vs-All."

OneVsRest is implemented as an Estimator. For the base classifier it takes instances of Classifier and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.

Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.

Example

The example below demonstrates how to load the Iris dataset, parse it as a DataFrame and perform multiclass classification using OneVsRest. The test error is calculated to measure the algorithm accuracy.

Refer to the Scala API docs for more details.

{% include_example scala/org/apache/spark/examples/ml/OneVsRestExample.scala %}

Refer to the Java API docs for more details.

{% include_example java/org/apache/spark/examples/ml/JavaOneVsRestExample.java %}

Regression

Linear regression

The interface for working with linear regression models and model summaries is similar to the logistic regression case.

Example

The following example demonstrates training an elastic net regularized linear regression model and extracting model summary statistics.

{% include_example scala/org/apache/spark/examples/ml/LinearRegressionWithElasticNetExample.scala %}

{% include_example java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java %}

{% include_example python/ml/linear_regression_with_elastic_net.py %}

Decision tree regression

Decision trees are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on decision trees.

Example