How does a decision tree algorithm determine the best split among features?

It always chooses the split with the highest number of features.
It evaluates all possible splits and selects the one that maximizes information gain or Gini impurity.
It randomly selects a split among features.
It uses the first feature encountered in the dataset for splitting.

Decision tree algorithms determine the best split by evaluating all possible splits and selecting the one that maximizes information gain (for entropy-based measures) or minimizes Gini impurity. This process is crucial for creating an effective and accurate decision tree model.

Add your answer