Boosting is an alternative technique that builds up tree models. You can use this in place of random forest to increase performance over basic tree models. In this article, we will learn how to use AdaBoost to increase our tree model performance in Sklearn.
AdaBoost, we can use the class
AdaBoostClassifier. We fit these models like any other model in sklearn. We can also do the same with
AdaBoostRegressor if we are predicted a contious value instead of classifying.
from sklearn.ensemble import AdaBoostClassifier from sklearn import datasets iris = datasets.load_iris() features = iris.data target = iris.target adaboost = AdaBoostClassifier(random_state=0) model = adaboost.fit(features, target) print(model.score())