Package smile.classification

Class RandomForest

java.lang.Object
smile.classification.AbstractClassifier<Tuple>
smile.classification.RandomForest
All Implemented Interfaces:
Serializable, ToDoubleFunction<Tuple>, ToIntFunction<Tuple>, Classifier<Tuple>, DataFrameClassifier, SHAP<Tuple>, TreeSHAP
public class RandomForest extends AbstractClassifier<Tuple> implements DataFrameClassifier, TreeSHAP
Random forest for classification. Random forest is an ensemble classifier that consists of many decision trees and outputs the majority vote of individual trees. The method combines bagging idea and the random selection of features.

Each tree is constructed using the following algorithm:

  1. If the number of cases in the training set is N, randomly sample N cases with replacement from the original data. This sample will be the training set for growing the tree.
  2. If there are M input variables, a number m << M is specified such that at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m is held constant during the forest growing.
  3. Each tree is grown to the largest extent possible. There is no pruning.
The advantages of random forest are:
  • For many data sets, it produces a highly accurate classifier.
  • It runs efficiently on large data sets.
  • It can handle thousands of input variables without variable deletion.
  • It gives estimates of what variables are important in the classification.
  • It generates an internal unbiased estimate of the generalization error as the forest building progresses.
  • It has an effective method for estimating missing data and maintains accuracy when a large proportion of the data are missing.
The disadvantages are
  • Random forests are prone to over-fitting for some datasets. This is even more pronounced on noisy data.
  • For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Therefore, the variable importance scores from random forest are not reliable for this type of data.
See Also:

  • Constructor Details

    • RandomForest

      public RandomForest(Formula formula, int k, RandomForest.Model[] models, ClassificationMetrics metrics, double[] importance)
      Constructor.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      k - the number of classes.
      models - forest of decision trees.
      metrics - the overall out-of-bag metric estimation.
      importance - the feature importance.

    • RandomForest

      public RandomForest(Formula formula, int k, RandomForest.Model[] models, ClassificationMetrics metrics, double[] importance, IntSet labels)
      Constructor.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      k - the number of classes.
      models - the base models.
      metrics - the overall out-of-bag metric estimation.
      importance - the feature importance.
      labels - the class label encoder.

  • Method Details

    • fit

      public static RandomForest fit(Formula formula, DataFrame data)
      Fits a random forest for classification.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      data - the data frame of the explanatory and response variables.
      Returns:
      the model.

    • fit

      public static RandomForest fit(Formula formula, DataFrame data, Properties params)
      Fits a random forest for classification.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      data - the data frame of the explanatory and response variables.
      params - the hyperparameters.
      Returns:
      the model.

    • fit

      public static RandomForest fit(Formula formula, DataFrame data, int ntrees, int mtry, SplitRule rule, int maxDepth, int maxNodes, int nodeSize, double subsample)
      Fits a random forest for classification.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      data - the data frame of the explanatory and response variables.
      ntrees - the number of trees.
      mtry - the number of input variables to be used to determine the decision at a node of the tree. floor(sqrt(p)) generally gives good performance, where p is the number of variables.
      rule - Decision tree split rule.
      maxDepth - the maximum depth of the tree.
      maxNodes - the maximum number of leaf nodes in the tree.
      nodeSize - the number of instances in a node below which the tree will not split, nodeSize = 5 generally gives good results.
      subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.
      Returns:
      the model.

    • fit

      public static RandomForest fit(Formula formula, DataFrame data, int ntrees, int mtry, SplitRule rule, int maxDepth, int maxNodes, int nodeSize, double subsample, int[] classWeight)
      Fits a random forest for regression.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      data - the data frame of the explanatory and response variables.
      ntrees - the number of trees.
      mtry - the number of input variables to be used to determine the decision at a node of the tree. floor(sqrt(p)) generally gives good performance, where p is the number of variables
      rule - Decision tree split rule.
      maxDepth - the maximum depth of the tree.
      maxNodes - the maximum number of leaf nodes in the tree.
      nodeSize - the number of instances in a node below which the tree will not split, nodeSize = 5 generally gives good results.
      subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.
      classWeight - Priors of the classes. The weight of each class is roughly the ratio of samples in each class. For example, if there are 400 positive samples and 100 negative samples, the classWeight should be [1, 4] (assuming label 0 is of negative, label 1 is of positive).
      Returns:
      the model.

    • fit

      public static RandomForest fit(Formula formula, DataFrame data, int ntrees, int mtry, SplitRule rule, int maxDepth, int maxNodes, int nodeSize, double subsample, int[] classWeight, LongStream seeds)
      Fits a random forest for classification.
      Parameters:
      formula - a symbolic description of the model to be fitted.
      data - the data frame of the explanatory and response variables.
      ntrees - the number of trees.
      mtry - the number of input variables to be used to determine the decision at a node of the tree. floor(sqrt(p)) generally gives good performance, where p is the number of variables.
      rule - Decision tree split rule.
      maxDepth - the maximum depth of the tree.
      maxNodes - the maximum number of leaf nodes in the tree.
      nodeSize - the number of instances in a node below which the tree will not split, nodeSize = 5 generally gives good results.
      subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.
      classWeight - Priors of the classes. The weight of each class is roughly the ratio of samples in each class. For example, if there are 400 positive samples and 100 negative samples, the classWeight should be [1, 4] (assuming label 0 is of negative, label 1 is of positive).
      seeds - optional RNG seeds for each regression tree.
      Returns:
      the model.

    • formula

      public Formula formula()
      Description copied from interface: DataFrameClassifier
      Returns the formula associated with the model.
      Specified by:
      formula in interface DataFrameClassifier
      Specified by:
      formula in interface TreeSHAP
      Returns:
      the formula associated with the model.

    • schema

      public StructType schema()
      Description copied from interface: DataFrameClassifier
      Returns the predictor schema.
      Specified by:
      schema in interface DataFrameClassifier
      Returns:
      the predictor schema.

    • metrics

      public ClassificationMetrics metrics()
      Returns the overall out-of-bag metric estimations. The OOB estimate is quite accurate given that enough trees have been grown. Otherwise, the OOB error estimate can bias upward.
      Returns:
      the out-of-bag metrics estimations.

    • importance

      public double[] importance()
      Returns the variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast measure of variable importance that is often very consistent with the permutation importance measure.
      Returns:
      the variable importance

    • size

      public int size()
      Returns the number of trees in the model.
      Returns:
      the number of trees in the model.

    • models

      public RandomForest.Model[] models()
      Returns the base models.
      Returns:
      the base models.

    • trees

      public DecisionTree[] trees()
      Description copied from interface: TreeSHAP
      Returns the decision trees.
      Specified by:
      trees in interface TreeSHAP
      Returns:
      the decision trees.

    • trim

      public RandomForest trim(int ntrees)
      Trims the tree model set to a smaller size in case of over-fitting. Or if extra decision trees in the model don't improve the performance, we may remove them to reduce the model size and also improve the speed of prediction.
      Parameters:
      ntrees - the new (smaller) size of tree model set.
      Returns:
      a new trimmed forest.

    • merge

      public RandomForest merge(RandomForest other)
      Merges two random forests.
      Parameters:
      other - the other forest to merge with.
      Returns:
      the merged forest.

    • predict

      public int predict(Tuple x)
      Description copied from interface: Classifier
      Predicts the class label of an instance.
      Specified by:
      predict in interface Classifier<Tuple>
      Parameters:
      x - the instance to be classified.
      Returns:
      the predicted class label.

    • soft

      public boolean soft()
      Description copied from interface: Classifier
      Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.
      Specified by:
      soft in interface Classifier<Tuple>
      Returns:
      true if soft classifier.

    • predict

      public int predict(Tuple x, double[] posteriori)
      Description copied from interface: Classifier
      Predicts the class label of an instance and also calculate a posteriori probabilities. Classifiers may NOT support this method since not all classification algorithms are able to calculate such a posteriori probabilities.
      Specified by:
      predict in interface Classifier<Tuple>
      Parameters:
      x - an instance to be classified.
      posteriori - a posteriori probabilities on output.
      Returns:
      the predicted class label

    • vote

      public int vote(Tuple x, double[] posteriori)
      Predict and estimate the probability by voting.
      Parameters:
      x - the instances to be classified.
      posteriori - a posteriori probabilities on output.
      Returns:
      the predicted class labels.

    • test

      public int[][] test(DataFrame data)
      Test the model on a validation dataset.
      Parameters:
      data - the test data set.
      Returns:
      the predictions with first 1, 2, ..., decision trees.

    • prune

      public RandomForest prune(DataFrame test)
      Returns a new random forest by reduced error pruning.
      Parameters:
      test - the test data set to evaluate the errors of nodes.
      Returns:
      a new pruned random forest.