Gradient Boosting from Concept to Observe (Half 2) | by Dr. Roi Yehoshua | Jul, 2023


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

Picture by Luca Bravo on Unsplash

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

On this a part of the article, we are going to discover the lessons in Scikit-Be taught that implement this algorithm, talk about their numerous parameters, and exhibit methods to use them to unravel a number of classification and regression issues.

Though the XGBoost library (which will likely 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 determination 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, equivalent to 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 could be ‘log_loss’ (the default) or ‘exponential’ (which can make gradient boosting behave just like the AdaBoost algorithm). In GradientBoostingRegressor, this operate could be ‘squared_loss’ (the default), ‘absolute_loss’, ‘huber’, or ‘quantile’.
  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 contemplate when looking for the most effective cut up in every node. The choices are to specify an integer for the…


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