Sklearn Gradient Boosting Regression

Gradient Boosting Regression, builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnGradientBoostingRegressor

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
loss	Loss	The loss function to be optimized. ‘ls’ refers to least squares regression.
learning_rate	Learning Rate	Learning rate shrinks the contribution of each tree by learning_rate.
n_estimators	Number of Estimators	The number of boosting stages to be run.
subsample	Subsample	The fraction of samples to be used for fitting the individual base learners.
criterion	Criterion	The function to measure the quality of a split.
min_samples_split	Min Samples Split	The minimum number of samples required to split an internal node.
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 (of all the input samples) required to be at a leaf node.
max_depth	Max Depth	Maximum depth of the individual regression estimators.
min_impurity_decrease	Min Impurity Decrease	A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
random_state	Random State	Controls the randomness of the bootstrapping of the samples used when building trees.
alpha	Alpha	The alpha-quantile of the huber loss function and the quantile loss function.
verbose	Verbose	Enable verbose output. If 1 then it prints progress and performance once in a while.
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.
warm_start	Warm Start	When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
presort	Presort	Whether to presort the data to speed up the finding of best splits in fitting.
validation_fraction	Validation Fraction	The proportion of training data to set aside as validation set for early stopping.
n_iter_no_change	N Iter No Change	Used to decide if early stopping will be used to terminate training when validation score is not improving.
tol	Tolerance	Tolerance for the early stopping. When the loss or score is not improving by at least tol for n_iter_no_change iterations, training stops.

Details

More details are available at : https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html