Sort this

https://mlcourse.ai/articles/topic3-dt-knn/#Tree-building-Algorithm

def build(L):
    create node t
    if the stopping criterion is True:
        assign a predictive model to t
    else:
        Find the best binary split L = L_left + L_right
        t.left = build(L_left)
        t.right = build(L_right)
    return t

random forest

https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/

max_split

For classification a good default is: m = sqrt(p)
For regression a good default is: m = p/3

Out-of-bag score

Feature importance

https://medium.com/the-artificial-impostor/feature-importance-measures-for-tree-models-part-i-47f187c1a2c3

https://stackoverflow.com/questions/15810339/how-are-feature-importances-in-randomforestclassifier-determined

superlearn