https://onclick360.com/cost-function-in-machine-learning/ ]]>

For a linear regression, there is a formula to explain the contribution of each independent variable

For a decision tree. there is a map to show the segmentation by each independent variable.

For a random forest, is there any clear ,easy and direct explanation about the fit result？ ]]>

What is the meaning behind the division with two?

]]>Can you please explain.

Thanks much, ]]>

actually, it is not only 2 features. it is 2 features, if no split is found, then it takes max_features=n (3).

from here:https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

“max_features : int, float, string or None, optional (default=”auto”)

The number of features to consider when looking for the best split:

If int, then consider max_features features at each split.

If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.

If “auto”, then max_features=sqrt(n_features).

If “sqrt”, then max_features=sqrt(n_features) (same as “auto”).

If “log2”, then max_features=log2(n_features).

If None, then max_features=n_features.

Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

”

this means, that it doesn’t neccesarily use only 2 features. which makes the entire point of the max_features option a bit useless in my opinion

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