Applied Data Science Coding with Python: CART Algorithm

The CART (Classification and Regression Tree) algorithm is a powerful tool for both classification and regression problems in machine learning. It is used to create a decision tree that can be used to make predictions based on the input data.

The CART algorithm works by recursively splitting the data into subsets based on different features and conditions. The algorithm starts with the entire dataset and splits it into two subsets based on a feature that results in the best split. This process is repeated until the subsets are pure, meaning they only contain data points of one class or target value.

The final output of the CART algorithm is a tree-like structure, where each internal node represents a feature or condition, and each leaf node represents a prediction or class. The path from the root to a leaf node represents the decision process followed by the algorithm to make a prediction.

There are several libraries available in Python to implement the CART algorithm, such as scikit-learn, NumPy and Pandas. These libraries provide pre-built functions and methods to build, train and evaluate a CART model.

It is important to note that CART algorithm is sensitive to the data and its complexity. Therefore, it’s important to preprocess and clean the data, also, it’s important to use the appropriate parameters to limit the complexity and overfitting.

In summary, the CART algorithm is a powerful tool for both classification and regression problems in machine learning. It creates a decision tree by recursively splitting the data into subsets based on different features and conditions, and the final output is a tree-like structure that can be used to make predictions. There are several libraries available in Python to implement the CART algorithm, and it is important to preprocess and clean the data, and use the appropriate parameters to limit the complexity and overfitting.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to apply CART Algorithm.