What is the term for the minimum number of samples required to be a leaf node in a decision tree?

The term for the minimum number of samples required to be a leaf node in a decision tree is referred to as "min_samples_leaf." This parameter plays a crucial role in the decision tree's structure by ensuring that each leaf node contains at least a specified number of samples. It helps to prevent overfitting, as a leaf with very few samples could correspond to noise in the data rather than a meaningful pattern. By setting a minimum threshold for the number of samples in a leaf node, it encourages the model to generalize better by not creating overly complex trees that fit the training data too closely.

Other options describe different aspects of decision trees: "min_samples_split" determines the minimum number of samples required to split an internal node; "max_depth" sets a limit on how deep the tree can grow; and "splitter" refers to the strategy used to choose the split at each node, typically either 'best' or 'random'. Understanding these terms is essential for effectively tuning decision trees in machine learning models.