交叉验证_交叉验证中support

交叉验证的形式

• K折交叉验证，初始采样分割成K个子样本，一个单独的子样本被保留作为验证模型的数据，其他K-1个样本用来训练。交叉验证重复K次，每个子样本验证一次，平均K次的结果或者使用其它结合方式，最终得到一个单一估测。这个方法的优势在于，同时重复运用随机产生的子样本进行训练和验证，每次的结果验证一次，10折交叉验证是最常用的。
• Holdout 验证：将原始数据随机分为两组,一组做为训练集,一组做为验证集,利用训练集训练分类器,然后利用验证集验证模型,记录最后的分类准确率为此Hold-OutMethod下分类器的性能指标.。Hold-OutMethod相对于K-fold Cross Validation 又称Double cross-validation ，或相对K-CV称 2-fold cross-validation(2-CV)
• 留一验证： 留一验证只使用原本样本中的一个样本来当做验证集， 而剩余的则留下来当做训练资料。 这个步骤一直持续到每个样本都被当做一次验证资料。 事实上，这等同于 K-fold 交叉验证是一样的，其中K为原本样本个数。 在某些情况下是存在有效率的演算法，如使用kernel regression 和Tikhonov regularization。

交叉验证的用途

• 选择超参数
• 评估模型的性能

代码

from __future__ import print_function

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC

print(__doc__)

# Loading the Digits dataset
digits = datasets.load_digits()

# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=0)

# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

scores = ['precision', 'recall']

for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()
#用网格搜索交叉验证获取最优超参数
    clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5,
                       scoring='%s_macro' % score)
    clf.fit(X_train, y_train)

    print("Best parameters set found on development set:")
    print()
    print(clf.best_params_)
    print()
    print("Grid scores on development set:")
    print()
    means = clf.cv_results_['mean_test_score']
    stds = clf.cv_results_['std_test_score']
    for mean, std, params in zip(means, stds, clf.cv_results_['params']):
        print("%0.3f (+/-%0.03f) for %r"
              % (mean, std * 2, params))
    print()

    print("Detailed classification report:")
    print()
    print("The model is trained on the full development set.")
    print("The scores are computed on the full evaluation set.")
    print()
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))
    print()
#在获取最优超参数之后，用5折交叉验证来评估模型
clf = SVC(kernel='rbf', C=1,gamma=1e-3)
scores = cross_val_score(clf, X, y, cv=5)
print("model of the best parameters")
print(scores)
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结果：

# Tuning hyper-parameters for precision

Best parameters set found on development set:

{'kernel': 'rbf', 'C': 10, 'gamma': 0.001}

Grid scores on development set:

0.986 (+/-0.016) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}
0.959 (+/-0.029) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}
0.988 (+/-0.017) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}
0.982 (+/-0.026) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}
0.988 (+/-0.017) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}
0.982 (+/-0.025) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}
0.988 (+/-0.017) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}
0.982 (+/-0.025) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}
0.975 (+/-0.014) for {'kernel': 'linear', 'C': 1}
0.975 (+/-0.014) for {'kernel': 'linear', 'C': 10}
0.975 (+/-0.014) for {'kernel': 'linear', 'C': 100}
0.975 (+/-0.014) for {'kernel': 'linear', 'C': 1000}

Detailed classification report:

The model is trained on the full development set.
The scores are computed on the full evaluation set.

             precision    recall  f1-score   support

          0       1.00      1.00      1.00        89
          1       0.97      1.00      0.98        90
          2       0.99      0.98      0.98        92
          3       1.00      0.99      0.99        93
          4       1.00      1.00      1.00        76
          5       0.99      0.98      0.99       108
          6       0.99      1.00      0.99        89
          7       0.99      1.00      0.99        78
          8       1.00      0.98      0.99        92
          9       0.99      0.99      0.99        92

avg / total       0.99      0.99      0.99       899

# Tuning hyper-parameters for recall

Best parameters set found on development set:

{'kernel': 'rbf', 'C': 10, 'gamma': 0.001}

Grid scores on development set:

0.986 (+/-0.019) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}
0.957 (+/-0.029) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}
0.987 (+/-0.019) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}
0.981 (+/-0.028) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}
0.987 (+/-0.019) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}
0.981 (+/-0.026) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}
0.987 (+/-0.019) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}
0.981 (+/-0.026) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}
0.972 (+/-0.012) for {'kernel': 'linear', 'C': 1}
0.972 (+/-0.012) for {'kernel': 'linear', 'C': 10}
0.972 (+/-0.012) for {'kernel': 'linear', 'C': 100}
0.972 (+/-0.012) for {'kernel': 'linear', 'C': 1000}

Detailed classification report:

The model is trained on the full development set.
The scores are computed on the full evaluation set.

             precision    recall  f1-score   support

          0       1.00      1.00      1.00        89
          1       0.97      1.00      0.98        90
          2       0.99      0.98      0.98        92
          3       1.00      0.99      0.99        93
          4       1.00      1.00      1.00        76
          5       0.99      0.98      0.99       108
          6       0.99      1.00      0.99        89
          7       0.99      1.00      0.99        78
          8       1.00      0.98      0.99        92
          9       0.99      0.99      0.99        92

avg / total       0.99      0.99      0.99       899
model of the best parameters
[ 0.97527473  0.95027624  0.98328691  0.99159664  0.95774648]
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