BoostingRegressor ( loss = 'tobit', yl = 0, yu = 100 ) # KTBoost algorithm (combined kernel and tree boosting) for classification with Newton updates model = KTBoost. Grabit model (Sigrist and Hirnschall, 2017), # with lower and upper limits at 0 and 100 model = KTBoost. predict ( Xpred ) More examples of models # More examples of models # Boosted Tobit model, i.e. fit ( Xtrain, ytrain ) # Make predictions # model. BoostingRegressor ( loss = 'ls' ) # Train models # model. Define models, train models, make predictions import KTBoost.KTBoost as KTBoost # Define model (see below for more examples) # Standard tree-boosting for regression with quadratic loss and hybrid gradient-Newton updates as in Friedman (2001) model = KTBoost. See also below for more information on the main parameters.
#Parametre grabit code#
The following code examples show how the package can be used. The two main classes are KTBoost.BoostingClassifier and KTBoost.BoostingRegressor. The package is build as an extension of the scikit-learn implementation of boosting algorithms and its workflow is very similar to that of scikit-learn. KTBoost: Combined Kernel and Tree Boosting. Gradient and Newton Boosting for Classification and Regression. Grabit: Gradient Tree Boosted Tobit Models for Default Prediction. Greedy function approximation: a gradient boosting machine.
#Parametre grabit install#
It can be installed using pip install -U KTBoostĪnd then loaded using import KTBoost.KTBoost as KTBoost Author Mixed continuous-categorical data ("censored regression"): negative Tobit likelihood (i.e., the Grabit model).(Unorderd) Categorical data ("classification"): logistic regression loss (log loss), exponential loss, cross entropy loss with softmax.Count data ("regression"): Poisson regression loss.Continuous data ("regression"): quadratic loss (L2 loss), absolute error (L1 loss), Huber loss, quantile regression loss, Gamma regression loss, negative Gaussian log-likelihood with both the mean and the standard deviation as functions of features.
The package implements the following loss functions: A hybrid version of the two for trees as base learners.A combination of the two (i.e., the KTBoost algorithm)Ĭoncerning the optimization step for finding the boosting updates, the package supports:.Reproducing kernel Hilbert space (RKHS) ridge regression functions (i.e., posterior means of Gaussian processes).DescriptionĬoncerning base learners, KTboost includes: This Python package implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
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