#调整随机森林的参数(调整max_features，结果未见明显差异)

#调整随机森林的参数(调整max_features，结果未见明显差异)

from sklearn import datasets
X, y = datasets.make_classification(n_samples=10000,n_features=20,n_informative=15,flip_y=.5, weights=[.2, .8])

import numpy as np
training = np.random.choice([True, False], p=[.8, .2],size=y.shape)

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(X[training], y[training])
preds = rf.predict(X[~training])
print ("Accuracy:\t", (preds == y[~training]).mean())

from sklearn.metrics import confusion_matrix
max_feature_params = [‘auto‘, ‘sqrt‘, ‘log2‘, .01, .5, .99]
confusion_matrixes = {}
for max_feature in max_feature_params:
    rf = RandomForestClassifier(max_features=max_feature)
    rf.fit(X[training], y[training])
    print ("Accuracy:\t", (preds == y[~training]).mean())
    confusion_matrixes= confusion_matrix(y[~training],rf.predict(X[~training]))
    print(max_feature,confusion_matrixes)
    print(‘--------------------------------------------------------------------‘)

from sklearn.metrics import confusion_matrix
y_true = [2, 0, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]
print(confusion_matrix(y_true, y_pred))

y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
print(confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]))
‘‘‘
Accuracy:     0.640324214792
Accuracy:     0.640324214792
auto [[278 403]
 [306 987]]
--------------------------------------------------------------------
Accuracy:     0.640324214792
sqrt [[280 401]
 [324 969]]
--------------------------------------------------------------------
Accuracy:     0.640324214792
log2 [[304 377]
 [320 973]]
--------------------------------------------------------------------
Accuracy:     0.640324214792
0.01 [[285 396]
 [324 969]]
--------------------------------------------------------------------
Accuracy:     0.640324214792
0.5 [[289 392]
 [305 988]]
--------------------------------------------------------------------
Accuracy:     0.640324214792
0.99 [[294 387]
 [295 998]]
--------------------------------------------------------------------
[[2 0 0]
 [0 0 1]
 [1 0 2]]
[[2 0 0]
 [0 0 1]
 [1 0 2]]
‘‘‘