min_sample_leaf of decisiontreeclassifier
时间: 2024-02-07 22:04:22 浏览: 24
The min_sample_leaf parameter in DecisionTreeClassifier is used to set the minimum number of samples required to be at a leaf node. This parameter ensures that a split is made only when the number of samples at the node is greater than or equal to the specified value.
Setting a higher value for min_sample_leaf can help prevent overfitting, as it forces the tree to generalize better and reduces the chances of memorizing the training data. However, setting it too high can lead to underfitting, where the model is too simple and fails to capture the complexity of the data.
The default value for min_sample_leaf is 1, which means that each leaf node must have at least one sample. The optimal value for this parameter depends on the complexity of the problem and the size of the dataset. It can be determined using techniques such as cross-validation or grid search.