normal fitting:

    Line #    Mem usage    Increment   Line Contents
    ================================================
    17   28.004 MiB    0.000 MiB   @profile
    18                             def normal_fit():
    19   28.777 MiB    0.773 MiB    X = random.random((1000,100))
    20   28.781 MiB    0.004 MiB    Y = random.random(1000) < 0.5
    21   28.785 MiB    0.004 MiB    rfc = RFC(n_estimators=100,n_jobs=1)
    22   28.785 MiB    0.000 MiB    rfc.n_classes_ = 2
    23   28.785 MiB    0.000 MiB    rfc.classes_ = array([False, True],dtype=bool)
    24   28.785 MiB    0.000 MiB    rfc.n_outputs_ = 1
    25   28.785 MiB    0.000 MiB    rfc.n_features_ = 100
    26   28.785 MiB    0.000 MiB    rfc.bootstrap = False
    27   37.668 MiB    8.883 MiB    rfc.fit(X,Y)

custom fitting:

    Line #    Mem usage    Increment   Line Contents
    ================================================
     4   28.004 MiB    0.000 MiB   @profile
     5                             def custom_fit():
     6   28.777 MiB    0.773 MiB    X = random.random((1000,100))
     7   28.781 MiB    0.004 MiB    Y = random.random(1000) < 0.5
     8   28.785 MiB    0.004 MiB    rfc = RFC(n_estimators=100,n_jobs=1)
     9   28.785 MiB    0.000 MiB    rfc.n_classes_ = 2
    10   28.785 MiB    0.000 MiB    rfc.classes_ = array([False, True],dtype=bool)
    11   28.785 MiB    0.000 MiB    rfc.n_outputs_ = 1
    12   28.785 MiB    0.000 MiB    rfc.n_features_ = 100
    13   73.266 MiB   44.480 MiB    for i in range(rfc.n_estimators):
    14   72.820 MiB   -0.445 MiB        rfc._make_estimator()
    15   73.262 MiB    0.441 MiB        rfc.estimators_[-1].fit(X,Y,check_input=False)