This is a gist to demonstrate some code on medium.

rf-explanation1.py

def split_vals(a,n):
    return a[:n].copy(), a[n:].copy()

def rmse(x,y): return math.sqrt(((x-y)**2).mean())

def print_score(m,X_train,y_train,X_valid,y_valid):
    res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),
                m.score(X_train, y_train), m.score(X_valid, y_valid)]
    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)
    print(res)
def fx(m,X_valid,y_valid):
    return(m.score(X_valid,y_valid))

def auto_train(a,b,X_train,y_train):
    ''' a and b are the min_ leaf and max_ features respectively'''
    q=RandomForestRegressor(n_jobs=-1, min_samples_leaf=a,max_features=b,oob_score=False)
    q.fit(X_train, y_train)
    return q