#调整随机森林的参数(调整n_estimators随机森林中树的数量默认10个树，精度递增显著，但并不是越多越好)，加上verbose=True，显示进程使用信息
#调整随机森林的参数(调整n_estimators随机森林中树的数量默认10个树，精度递增显著) 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
from sklearn.metrics import confusion_matrix n_estimator_params = range(1, 100,5)
confusion_matrixes = {}
for n_estimator in n_estimator_params:
rf = RandomForestClassifier(n_estimators=n_estimator,n_jobs=-1, verbose=True)
rf.fit(X[training], y[training])
print ("Accuracy:\t", (rf.predict(X[~training]) == y[~training]).mean()) '''
======================== RESTART: E:/python/pp138.py ========================
[Parallel(n_jobs=-1)]: Done 1 out of 1 | elapsed: 0.0s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s finished
Accuracy: 0.590083456063
[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.1s finished
[Parallel(n_jobs=2)]: Done 6 out of 6 | elapsed: 0.0s finished
Accuracy: 0.618065783014
[Parallel(n_jobs=-1)]: Done 11 out of 11 | elapsed: 0.3s finished
[Parallel(n_jobs=2)]: Done 11 out of 11 | elapsed: 0.0s finished
Accuracy: 0.682866961217
[Parallel(n_jobs=-1)]: Done 16 out of 16 | elapsed: 0.5s finished
[Parallel(n_jobs=2)]: Done 16 out of 16 | elapsed: 0.0s finished
Accuracy: 0.692194403535
[Parallel(n_jobs=-1)]: Done 21 out of 21 | elapsed: 0.6s finished
[Parallel(n_jobs=2)]: Done 21 out of 21 | elapsed: 0.0s finished
Accuracy: 0.702012763868
[Parallel(n_jobs=-1)]: Done 26 out of 26 | elapsed: 0.9s finished
[Parallel(n_jobs=2)]: Done 26 out of 26 | elapsed: 0.0s finished
Accuracy: 0.697594501718
[Parallel(n_jobs=-1)]: Done 31 out of 31 | elapsed: 1.0s finished
[Parallel(n_jobs=2)]: Done 31 out of 31 | elapsed: 0.0s finished
Accuracy: 0.710358370152
[Parallel(n_jobs=-1)]: Done 36 out of 36 | elapsed: 1.1s finished
[Parallel(n_jobs=2)]: Done 36 out of 36 | elapsed: 0.0s finished
Accuracy: 0.704958271969
[Parallel(n_jobs=-1)]: Done 41 out of 41 | elapsed: 1.3s finished
[Parallel(n_jobs=2)]: Done 41 out of 41 | elapsed: 0.0s finished
Accuracy: 0.707412862052
[Parallel(n_jobs=-1)]: Done 46 out of 46 | elapsed: 1.5s finished
[Parallel(n_jobs=2)]: Done 46 out of 46 | elapsed: 0.0s finished
Accuracy: 0.716740304369
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.6s
[Parallel(n_jobs=-1)]: Done 51 out of 51 | elapsed: 1.8s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 51 out of 51 | elapsed: 0.0s finished
Accuracy: 0.713303878252
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.5s
[Parallel(n_jobs=-1)]: Done 56 out of 56 | elapsed: 1.8s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 56 out of 56 | elapsed: 0.0s finished
Accuracy: 0.713303878252
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.5s
[Parallel(n_jobs=-1)]: Done 61 out of 61 | elapsed: 2.0s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 61 out of 61 | elapsed: 0.0s finished
Accuracy: 0.717231222386
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.5s
[Parallel(n_jobs=-1)]: Done 66 out of 66 | elapsed: 2.3s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 66 out of 66 | elapsed: 0.0s finished
Accuracy: 0.711340206186
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.6s
[Parallel(n_jobs=-1)]: Done 71 out of 71 | elapsed: 2.5s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 71 out of 71 | elapsed: 0.0s finished
Accuracy: 0.720667648503
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.5s
[Parallel(n_jobs=-1)]: Done 76 out of 76 | elapsed: 2.4s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 76 out of 76 | elapsed: 0.0s finished
Accuracy: 0.721649484536
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.7s
[Parallel(n_jobs=-1)]: Done 81 out of 81 | elapsed: 3.0s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 81 out of 81 | elapsed: 0.0s finished
Accuracy: 0.721649484536
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.5s
[Parallel(n_jobs=-1)]: Done 86 out of 86 | elapsed: 2.8s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 86 out of 86 | elapsed: 0.0s finished
Accuracy: 0.716740304369
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.5s
[Parallel(n_jobs=-1)]: Done 91 out of 91 | elapsed: 3.1s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 91 out of 91 | elapsed: 0.0s finished
Accuracy: 0.72410407462
[Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 1.4s
[Parallel(n_jobs=-1)]: Done 96 out of 96 | elapsed: 3.1s finished
[Parallel(n_jobs=2)]: Done 46 tasks | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 96 out of 96 | elapsed: 0.0s finished
Accuracy: 0.718213058419
'''
#调整随机森林的参数(调整n_estimators随机森林中树的数量默认10个树，精度递增显著，但并不是越多越好)，加上verbose=True，显示进程使用信息的更多相关文章
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