I'm confused about a particular part of the documentation. I want to know what min_samples_leaf refers to when it's input as a float.

min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.
If int, then consider min_samples_leaf as the minimum number.
If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

My question is this: n_samples is not a valid parameter of this model. If I set min_samples_leaf to 0.1, does that mean that at least 10% of the max_samples (i.e., the total number of samples taken during the bootstrap) have to be in each child, or does it mean that at least 10% of the samples that are currently in that node being considered for splitting must be in each child?


1 Answer 1


n_samples is an implicit parameter of the model when calling fit and predict functions, calculated based on X or y matrices, e.g.

X: {array-like, sparse matrix} of shape (n_samples, n_features)

So, when float, min_samples_leaf is the percentage of the total number of samples during training; not the number of samples in the node that is to be split.

This can also be seen in the following code segment of decision tree classifier:

# Determine output settings
n_samples, self.n_features_in_ = X.shape
min_samples_split = int(ceil(self.min_samples_split * n_samples))
  • 1
    $\begingroup$ Thanks @gunes! I appreciate the clarification. $\endgroup$
    – NaiveBae
    Jun 10 at 17:35

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.