Applied Data Science Coding with Python: Regression with CART Algorithm

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Applied Data Science Coding with Python: Regression with CART Algorithm

Regression with the Classification and Regression Tree (CART) algorithm is a method for solving 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 starts by recursively splitting the data into subsets based on different features and conditions, similar to the classification task. However, instead of trying to find a pure subset of data points with the same class label, the CART algorithm aims to find subsets of data points with similar target variable values.

After the tree is built, it can be used to make predictions for new data points by following the path from the root to a leaf node. The value of the target variable at the leaf node is the predicted value for the new data point.

In order to use the CART algorithm for regression in Python, you need to have a dataset that includes both the input data and the target variable values. You also need to decide on the parameters such as the maximum depth of the tree, the minimum number of samples required to split an internal node, etc.

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

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to apply CART Algorithm in regression problems.



Applied Data Science Coding with Python: Regression with CART Algorithm

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How to use Classification and Regression Tree (CART) in Python

Applied Data Science Coding with Python: CART Algorithm

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