from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, min_samples_leaf=10,
random_state=1)
model.fit(x_train, y_train)
print(model.score)
#Accuracy of prediction
y_pred = model.predict(x_test)
#Mean Standard Error
mean_squared_error(y_pred, y_test)
model.score(x_test, y_test)
Out[423]: 0.80038542832276516

from sklearn.ensemble import GradientBoostingClassifier #For Classification
clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
max_depth=1)
clf.fit(x_train, y_train)

clf.fit(x_train, y_train)
Traceback (most recent call last):

File "<ipython-input-425-9249b506d83f>", line 1, in <module>
clf.fit(x_train, y_train)

File "C:Anaconda3libsite-packagessklearnensemblegradient_boosting.py",
line 973, in fit
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)

File "C:Anaconda3libsite-packagessklearnutilsvalidation.py", line 526,
in check_X_y
y = column_or_1d(y, warn=True)

File "C:Anaconda3libsite-packagessklearnutilsvalidation.py", line 562,
in column_or_1d
raise ValueError("bad input shape {0}".format(shape))

ValueError: bad input shape (37533, 3)

print(x_train)
        No  Yes
32912  1.0  0.0
35665  1.0  0.0
32436  1.0  0.0
25885  1.0  0.0
24896  1.0  0.0
51734  1.0  0.0
4235   1.0  0.0
51171  1.0  0.0
33221  0.0  1.0

print(y_train)
       Fatal  Incident  Non-Fatal
32912    0.0       0.0        1.0
35665    0.0       0.0        1.0
32436    0.0       0.0        1.0