A decision tree is a type of tree structure that resembles a flowchart, with core nodes representing features, branches representing rules, and leaf nodes representing the algorithm’s outcome. It is a flexible supervised machine-learning approach that may be applied to regression and classification issues alike. It is among the most potent algorithms. Additionally, Random Forest uses it to train on various subsets of training data, making it one of the most potent machine learning algorithms.
It is a tool for supervised learning algorithms that may be applied to tasks involving regression and classification. It creates a tree structure that resembles a flowchart, with each internal node signifying an attribute test, each branch denoting a test result, and each leaf node (terminal has a class label (node). When a stopping criterion—such as the maximum depth of the tree or the minimum number of samples needed to split a node—is satisfied, the training data is recursively split into subsets based on the values of the attributes.
A metric like entropy or Gini impurity, which gauges the degree of unpredictability or impurity in the subsets, is used by the Decision Tree method to determine which characteristic is optimal for splitting the data during training. The objective is to identify the characteristic that optimizes the gain in information or the decrease in impurity following the split.
will provide updates in the future regarding the location and method of application of this to our data set.