Sklearn Random Forest Regression

Random Forest Regression, fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnRandomForestRegression

Fields

Name

Title

Description

targetCol

Target Column

The label column for model fitting

featureCols

Feature Columns

Feature columns of type - all numeric, boolean and vector

splitRatio

Split Ratio

Split Ratio

n_estimators

Number of Estimators

Specifies the number of trees in the forest.

criterion

Criterion

The function to measure the quality of a split.

max_depth

Max Depth

The maximum depth of the tree. If not set, nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

min_samples_split

Min Samples Split

The minimum number of samples required to split an internal node. Higher values prevent a tree from growing too complex.

min_samples_leaf

Min Samples Leaf

The minimum number of samples required to be at a leaf node.

min_weight_fraction_leaf

Min Weight Fraction Leaf

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

max_features

Max Features

The number of features to consider when looking for the best split.

max_leaf_nodes

Max Leaf Nodes

Grow trees with max_leaf_nodes in best-first fashion. If not set, then unlimited number of leaf nodes.

min_impurity_decrease

Min Impurity Decrease

A threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

bootstrap

Bootstrap

Whether bootstrap samples are used when building trees.

oob_score

Oob Score

Whether to use out-of-bag samples to estimate the R^2 on unseen data.

random_state

Random State

Controls the randomness of the bootstrapping of the samples used when building trees (if bootstrap=True). Default value is None

warm_start

Warm Start

When warm_start is true, the existing fitted model attributes an are used to initialise the new model in a subsequent call to fit.