<jupyter_start><jupyter_text>This is an example More text<jupyter_code>for a in range(20):
    print(a)
import numpy as np 
from sklearn.ensemble import GradientBoostingClassifier
x = np.random.rand(100,50)
y = np.ones(100).astype(str)
y[:50] = "IN"
y[50:] = "OUT"
clf = GradientBoostingClassifier()
clf.fit(x, y)
clf.score(x,y)
np.save("test.npy", clf)
m = np.load("test.npy", allow_pickle=True)[0][0]
m.score(x,y)<jupyter_output>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:72: FutureWarning: Beginning in version 0.22, arrays of bytes/strings will be converted to decimal numbers if dtype='numeric'. It is recommended that you convert the array to a float dtype before using it in scikit-learn, for example by using your_array = your_array.astype(np.float64).
  return f(**kwargs)
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