Identify relevant features for classification, and build the decision tree using the chosen algorithm on the training data. Evaluate the model's performance using metrics like accuracy, precision ...

Learn the steps to effectively use decision trees for data predictions in data science. Discover how to choose a problem, prepare the data, build, prune, evaluate, and interpret the tree.

Decision Tree is the simple but powerful classification algorithm of machine learning where a tree or graph-like structure is constructed to display algorithms and reach possible consequences of a ...

Given a training data set, it constructs a decision tree for classification or regression in a single batch or incrementally. It loads data from CSV files. It expects the first row in the CSV to be a ...

This repository provides a theoretical overview of the decision tree algorithm for prediction. It aims to explain the concepts, principles, and steps involved in using decision trees for making ...

The algorithm selects the best attributes for splitting to construct a tree-like structure, and then classifies or regresses the new data. In the agricultural economy, decision tree algorithms can ...

Decision tree is a machine learning algorithm that can effectively predict student performance. However, the existing performance prediction models rarely analyze the impact of multiple factors on ...