What is min_samples_split in Decision Trees?
Updated May 16, 2026
Short answer
It defines the minimum number of samples required to split an internal node.
Deep explanation
min_samples_split prevents the tree from creating splits that are statistically unreliable. If a node has fewer samples than this threshold, it becomes a leaf node. This reduces overfitting by avoiding splits based on very small datasets that may capture noise rather than patterns.
Real-world example
In marketing analytics, it prevents creating customer segments based on too few users.
Common mistakes
- Setting it too low, causing overfitting.
Follow-up questions
- How is it different from min_samples_leaf?
- What happens if set too high?
- Should it be tuned with max_depth?