Search Java Classes and Packages
#Org.apache.spark.mllib.tree Classes and Interfaces - 24 results found.
| Name | Description | Type | Package | Framework |
| AbsoluteError | Class for absolute error loss calculation (for regression). | Class | org.apache.spark.mllib.tree.loss | Apache Spark |
| Algo | Enum to select the algorithm for the decision treeSee Also:Serialized Form | Class | org.apache.spark.mllib.tree.configuration | Apache Spark |
| BoostingStrategy | Configuration options for GradientBoostedTrees. | Class | org.apache.spark.mllib.tree.configuration | Apache Spark |
| DecisionTree | A class which implements a decision tree learning algorithm for classification and regression. | Class | org.apache.spark.mllib.tree | Apache Spark |
| DecisionTreeModel | Decision tree model for classification or regression. | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| Entropy | Class for calculating entropy during binary classification. | Class | org.apache.spark.mllib.tree.impurity | Apache Spark |
| FeatureType | Enum to describe whether a feature is "continuous" or "categorical"See Also:Serialized Form | Class | org.apache.spark.mllib.tree.configuration | Apache Spark |
| Gini | Class for calculating the during binary classification. | Class | org.apache.spark.mllib.tree.impurity | Apache Spark |
| GradientBoostedTrees | A class that implements Stochastic Gradient Boosting | Class | org.apache.spark.mllib.tree | Apache Spark |
| GradientBoostedTreesModel | Represents a gradient boosted trees model. | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| Impurity | Trait for calculating information gain. | Interface | org.apache.spark.mllib.tree.impurity | Apache Spark |
| InformationGainStats | Information gain statistics for each split param: gain information gain value | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| LogLoss | Class for log loss calculation (for classification). | Class | org.apache.spark.mllib.tree.loss | Apache Spark |
| Loss | Trait for adding "pluggable" loss functions for the gradient boosting algorithm. | Interface | org.apache.spark.mllib.tree.loss | Apache Spark |
| Losses | Class | org.apache.spark.mllib.tree.loss | Apache Spark | |
| Node | Node in a decision tree. | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| Predict | Predicted value for a node param: predict predicted value | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| QuantileStrategy | Enum for selecting the quantile calculation strategySee Also:Serialized Form | Class | org.apache.spark.mllib.tree.configuration | Apache Spark |
| RandomForest | A class that implements a Random Forest learning algorithm for classification and regression. | Class | org.apache.spark.mllib.tree | Apache Spark |
| RandomForestModel | Represents a random forest model. | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| Split | Split applied to a feature param: feature feature index | Class | org.apache.spark.mllib.tree.model | Apache Spark |
| SquaredError | Class for squared error loss calculation. | Class | org.apache.spark.mllib.tree.loss | Apache Spark |
| Strategy | Stores all the configuration options for tree construction param: algo Learning goal. | Class | org.apache.spark.mllib.tree.configuration | Apache Spark |
| Variance | Class for calculating variance during regressionSee Also:Serialized Form | Class | org.apache.spark.mllib.tree.impurity | Apache Spark |