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A course about decision trees, random forest, gradient boosting decision trees, XGBoost and extra trees in Python
In this practical course, we are going to focus on the decision tree machine learning models using Python programming language.
Decision trees are a particular and very effective type of model of the machine learning landscape. They try to predict the output variable according to particular binary decision rules to apply to the features. The best split that satisfies the rule is found during the training phase.
Decision trees express their best power when used in an ensemble. This way, we get models like random forest and extremely randomized trees (if we use bagging) and gradient boosting decision trees (if we use boosting).
With this course, you are going to learn:
Theory of the decision trees, with several splitting criteria for regression and classification
Hyperparameters of the decision trees
Random forest and its hyperparameters
Extremely randomized tree and its hyperparameters
Gradient Boosting Decision Tree and its hyperparameters
XGBoost and its hyperparameters
All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.
This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the larger course.
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