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Decision Trees Explained
Learn everything about Decision Trees for Machine Learning
In this post, I will explain Decision Trees in simple terms. It could be considered a Decision Trees for dummies post, however, I’ve never really liked that expression.
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Introduction and Intuition
In the Machine Learning world, Decision Trees are a kind of non parametric models, that can be used for both classification and regression.
This means that Decision trees are flexible models that don’t increase their number of parameters as we add more features (if we build them correctly), and they can either output a categorical prediction (like if a plant is of a certain kind or not) or a numerical prediction (like the price of a house).
They are constructed using two kinds of elements: nodes and branches. At each node, one of the features of our data is evaluated in order to split the observations in the training process or to make an specific data point follow a certain path when making a prediction.
