scikit學習決策樹模型評估

Question

下面是相關的代碼和文檔，想知道默認cross_val_score沒有明確指定score，輸出數組意味着精度，AUC或一些其他指標？

使用Python 2.7與miniconda解釋器。

http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

>>> from sklearn.datasets import load_iris 
>>> from sklearn.cross_validation import cross_val_score 
>>> from sklearn.tree import DecisionTreeClassifier 
>>> clf = DecisionTreeClassifier(random_state=0) 
>>> iris = load_iris() 
>>> cross_val_score(clf, iris.data, iris.target, cv=10) 
...        
... 
array([ 1.  , 0.93..., 0.86..., 0.93..., 0.93..., 
     0.93..., 0.93..., 1.  , 0.93..., 1.  ]) 

問候， 林

Answer 1

從user guide：

默認情況下，在每個CV的迭代中計算的得分是估計器的得分 方法。它是可以通過使用 得分參數可以改變：

從DecisionTreeClassifier documentation：

返回給定的測試數據和標籤的平均準確度。在 多標籤分類中，這是子集精度，這是一個嚴格的度量標準，因爲您需要對每個標籤集合爲 的每個樣本正確預測。

不要被「平均準確度」所迷惑，它只是計算準確度的常規方法。按照鏈接到source：

from .metrics import accuracy_score 
    return accuracy_score(y, self.predict(X), sample_weight=sample_weight) 

現在source的metrics.accuracy_score

def accuracy_score(y_true, y_pred, normalize=True, sample_weight=None): 
    ... 
    # Compute accuracy for each possible representation 
    y_type, y_true, y_pred = _check_targets(y_true, y_pred) 
    if y_type.startswith('multilabel'): 
     differing_labels = count_nonzero(y_true - y_pred, axis=1) 
     score = differing_labels == 0 
    else: 
     score = y_true == y_pred 

    return _weighted_sum(score, sample_weight, normalize) 

如果你still aren't convinced:

def _weighted_sum(sample_score, sample_weight, normalize=False): 
    if normalize: 
     return np.average(sample_score, weights=sample_weight) 
    elif sample_weight is not None: 
     return np.dot(sample_score, sample_weight) 
    else: 
     return sample_score.sum() 

注：accuracy_score規範化參數默認爲True，因此只需b。返回np.average oolean numpy數組，因此它只是正確預測的平均數量。

Answer 2

如果打分的說法沒有給出，cross_val_score將默認使用您所使用的估計的.score方法。對於DecisionTreeClassifier，它的意思是精確度（如下面的文檔字符串）：

In [11]: DecisionTreeClassifier.score? 
Signature: DecisionTreeClassifier.score(self, X, y, sample_weight=None) 
Docstring: 
Returns the mean accuracy on the given test data and labels. 

In multi-label classification, this is the subset accuracy 
which is a harsh metric since you require for each sample that 
each label set be correctly predicted. 

Parameters 
---------- 
X : array-like, shape = (n_samples, n_features) 
    Test samples. 

y : array-like, shape = (n_samples) or (n_samples, n_outputs) 
    True labels for X. 

sample_weight : array-like, shape = [n_samples], optional 
    Sample weights. 

Returns 
------- 
score : float 
    Mean accuracy of self.predict(X) wrt. y.