KTBoost 0.0.6

Implements several boosting algorithms in Python

KTBoost

This Python package implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

Concerning base learners, this includes:

• Trees
• Kernel Ridge regression
• A combination of the two (i.e., the KTBoost algorithm)

Concerning the optimization step for finding the boosting updates, the package supports:

• Gradient descent
• Newton-Rahson method
• A hybrid version of the two for trees as base learners

The package implements the following loss functions:

• Continuous data (regression): quadratic loss (L2 loss), absolute error (L1 loss), Huber loss, quantile regression loss, Gamma regression loss, negative Gaussian likelihood with both the mean and the standard deviation as functions of features
• Count data (regression): Poisson regression loss
• (Unorderd) Categorical data (classification): logistic regression loss (log loss), exponential loss, cross entropy loss with softmax
• Mixed continuous-categorical data (censored regression): negative Tobit likelihood (i.e., the Grabit model)

The package re-uses code from scikit-learn and its workflow is very similar to that of scikit-learn.

It can be installed using pip install KTBoost and then loaded using import KTBoost.KTBoost as KTBoost. The two main classes are KTBoost.BoostingClassifier and KTBoost.BoostingRegressor.

The following code example defines a model, trains it, and makes predictions.

import KTBoost.KTBoost as KTBoost
model = KTBoost.BoostingRegressor(loss="ls",base_learner="tree")
model.fit(Xtrain,ytrain)
model.predict(Xpred)