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Is there any way to get samples under each leaf?
I have trained a decision tree using a dataset. Now I want to see which samples fall under which leaf of the tree. From here I want the red circled samples. I am using Python’s Sklearn’s implementation of decision tree .
What happens if we omit Min _ samples _ leaf argument?
If we omit the min_samples_leaf argument, it will default to 1, and that means the decision tree/random forest will only need 1 observation to justify a split — which does seem somewhat prone to overfitting. My Question is: How can we decide what the min_samples_leaf argument for the general case should be?
Is it okay to have more than k observations in KNN?
You are mixing up kNN classification and k-means. There is nothing wrong with having more than k observations near a center in k-means. In fact, this it the usual case; you shouldn’t choose k too large. If you have 1 million points, a k of 100 may be okay.
Which is an example of over fitting in sklearn?
I have seen a few useful examples on the SKlearn documentation page where in some situations, over-fitting can be handled to a reasonable extent by making sure that the splits leave each node with at least a certain number of samples/observations.
How to use sklearn.tree.decisiontreeclassifier?
The underlying Tree object. Please refer to help (sklearn.tree._tree.Tree) for attributes of Tree object and Understanding the decision tree structure for basic usage of these attributes. A decision tree regressor.
Why do you need to extract rules from decision tree?
The rules extraction from the Decision Tree can help with better understanding how samples propagate through the tree during the prediction. It can be needed if we want to implement a Decision Tree without Scikit-learn or different than Python language.
How to extract rules from scikit-learn decision tree?
The Scikit-Learn Decision Tree class has an export_text (). It returns the text representation of the rules. You can pass the feature names as the argument to get better text representation: The output, with our feature names instead of generic feature_0, feature_1, … :