Gradient Boosting from Theory to Practice (Part 2) | by Dr. Roi Yehoshua | Jul, 2023

Use the gradient boosting lessons in Scikit-Be taught to unravel completely different classification and regression issues

Within the first part of this text, we offered the gradient boosting algorithm and confirmed its implementation in pseudocode.

On this a part of the article, we’ll discover the lessons in Scikit-Be taught that implement this algorithm, focus on their varied parameters, and show the way to use them to unravel a number of classification and regression issues.

Though the XGBoost library (which might be coated in a future article) gives a extra optimized and extremely scalable implementation of gradient boosting, for small to medium-sized information units it’s usually simpler to make use of the gradient boosting lessons in Scikit-Be taught, which have an easier interface and a considerably fewer variety of hyperparameters to tune.

Scikit-Be taught gives the next lessons that implement the gradient-boosted resolution bushes (GBDT) mannequin:

1. GradientBoostingClassifier is used for classification issues.
2. GradientBoostingRegressor is used for regression issues.

Along with the usual parameters of decision trees, reminiscent of criterion, max_depth (set by default to three) and min_samples_split, these lessons present the next parameters:

1. loss — the loss operate to be optimized. In GradientBoostingClassifier, this operate will be ‘log_loss’ (the default) or ‘exponential’ (which is able to make gradient boosting behave like AdaBoost). In GradientBoostingRegressor, this operate will be ‘squared_loss’ (the default), ‘absolute_loss’, ‘huber’, or ‘quantile’ (see this article for the variations between these loss features).
2. n_estimators — the variety of boosting iterations (defaults to 100).
3. learning_rate — an element that shrinks the contribution of every tree (defaults to 0.1).
4. subsample — the fraction of samples to make use of for coaching every tree (defaults to 1.0).
5. max_features — the variety of options to think about when looking for one of the best cut up in every…