http://blog.csdn.net/pipisorry/article/details/52250983

选择合适的estimator

通常机器学习最难的一部分是选择合适的estimator，不同的estimator适用于不同的数据集和问题。

sklearn官方文档提供了一个图[flowchart]，可以快速地根据你的数据和问题选择合适的estimator，单击相应的区域还可以获得更具体的内容。

代码中我一般这么写

def gen_estimators():
    '''
    List of the different estimators.
    '''
    estimators = [
        # ('Lasso regression', linear_model.Lasso(alpha=0.1), True),
        ('Ridge regression', linear_model.Ridge(alpha=0.1), True),
        # ('Hinge regression', linear_model.Hinge(), True),
        # ('LassoLars regression', linear_model.LassoLars(alpha=0.1), True),
        ('OrthogonalMatchingPursuitCV regression', linear_model.OrthogonalMatchingPursuitCV(), True),
        ('BayesianRidge regression', linear_model.BayesianRidge(), True),
        ('PassiveAggressiveRegressor regression', linear_model.PassiveAggressiveRegressor(), True),
        ('HuberRegressor regression', linear_model.HuberRegressor(), True),
        # ('LogisticRegression regression', linear_model.LogisticRegression(), True),
    ]
    return estimators

然后如下遍历算法

def cross_validate():
    for name, clf, flag in gen_estimators():
)
        clf.fit(x_train, y_train)
        print(name, '\n', clf.coef_)
        # scores = cross_val_score(clf, x, y, cv=5, scoring='roc_auc')
        y_score = clf.predict(x_test)
        y_score = np.select([y_score < 0.0, y_score > 1.0, True], [0.0, 1.0, y_score])
        scores = metrics.roc_auc_score(y_true=[1.0 if _ > 0.0 else 0.0 for _ in y_test], y_score=y_score)
        )
X_train.shape, y_train.shape
((90, 4), (90,))
X_test.shape, y_test.shape
((60, 4), (60,))

clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
clf.score(X_test, y_test)
0.96...

sklearn交叉验证

scores , scoring=rocAucScorer)

自定义CV策略

（cv是整数的话默认使用KFold）：

>>> n_samples = iris.data.shape[0]
>>> cv = cross_validation.ShuffleSplit(n_samples, n_iter=3, test_size=0.3, random_state=0)
>>> cross_validation.cross_val_score(clf, iris.data, iris.target, cv=cv)
array([ 0.97...,  0.97...,  1.        ])

另一个接口cross_val_predict ，可以返回每个元素作为test set时的确切预测值（只有在CV的条件下数据集中每个元素都有唯一预测值时才不会出现异常），进而评估estimator：
>>> predicted = cross_validation.cross_val_predict(clf, iris.data, iris.target, cv=10)
>>> metrics.accuracy_score(iris.target, predicted)
0.966...

[scikit-klean交叉验证]

皮皮blog

Scikit-learn：并行调参Grid Search

Grid Search: Searching for estimator parameters

scikit-learn中提供了pipeline(for estimator connection) & grid_search(searching best parameters)进行并行调参

如使用scikit-learn做文本分类时：vectorizer取多少个word呢？预处理时候要过滤掉tf>max_df的words，max_df设多少呢？tfidftransformer只用tf还是加idf呢？classifier分类时迭代几次？学习率怎么设？……
“循环一个个试”，这就是grid search要做的基本东西。

皮皮blog

from: http://blog.csdn.net/pipisorry/article/details/52250983

ref: [scikit-learn User Guide]

[Model selection and evaluation]

[3.1. Cross-validation: evaluating estimator performance]*

[3.2. Grid Search: Searching for estimator parameters]*

[3.4. Model persistence]

[Parameter estimation using grid search with cross-validation*]

[Sample pipeline for text feature extraction and evaluation*]

[python并行调参——scikit-learn grid_search]*