Question

Running a binary classification tree algorithm is the easy part. Do you know how does a tree splitting takes place i.e. how does the tree decide which variable to split at the root node and succeeding nodes?

Answer 1

The tree splitting in a binary classification algorithm involves selecting the best variable and value to optimize the separation of classes at each node based on criteria like Gini impurity or information gain.

Step-by-step explanation:

When building a binary classification tree, the algorithm employs a process called tree splitting to determine the optimal variable and value for partitioning the data at each node. The objective is to create nodes that result in the most effective separation of classes. Common criteria for evaluating the effectiveness of a split include Gini impurity and information gain. Gini impurity measures the probability of misclassifying a randomly chosen element, while information gain assesses the reduction in uncertainty about the class labels.

The algorithm systematically evaluates all possible splits across variables and values and selects the one that maximizes the chosen criteria. This recursive process continues for each node until a stopping condition, such as reaching a specified tree depth or a minimum number of samples per node, is met.