A Decision Tree is a supervised machine learning algorithm used for both classification and regression tasks. It works by splitting the data into subsets based on feature values, aiming to create a tree-like model of decisions. Learning about Decision Trees is often a key part of AI training courses, as it is one of the most powerful yet interpretable algorithms.
The tree starts with a root node that splits the data based on the most significant feature.
The data is then recursively split at each node, where the best feature is chosen at each step to minimize the impurity of the splits.
In classification, impurity measures like Gini Impurity or Entropy (Information Gain) are used to select the feature for the split.
In regression, the mean squared error (MSE) is used to determine the best splits.
Example: A decision tree can be used to predict whether a customer will buy a product based on features such as age, income, and location. Learning how to implement Decision Trees is a valuable skill covered in AI training courses, enabling students to build practical predictive models.