Question

我试图了解二进制分类器中#include <stdio.h> #include <string.h> int main() { char s[100], voc[10], aux[100]; strcpy(voc,"aeiouAEIOU"); scanf("%s",&s[100]); for(int i=0;i<strlen(s);i++) if(strchr(voc,s[i])) { strcpy(aux,s+i+1); s[i+1]='p'; s[i+2]=s[i]; strcpy(s+i+3,aux); i+=2; } printf("%s",s); return 0; }和decision_function的用法，并遇到了predict_proba中的阈值

现在假设precision_recall_curve计算到超平面的距离，decision_function给出数据点属于某个组的概率。

predict_proba返回具有不同阈值的阈值数组。

如果阈值是这些数据点的分类概率，那么阈值如何取负值或小于0或大于1的值。

此外，我们可以使用什么来微调二进制分类器？ precision_recall_curve或decision_function吗？

示例：

predict_proba

这里的阈值为

from sklearn.metrics import precision_recall_curve

precision, recall, thresholds = precision_recall_curve(y_test, y_scores_lr)
closest_zero = np.argmin(np.abs(thresholds))
closest_zero_p = precision[closest_zero]
closest_zero_r = recall[closest_zero]

print('Thresholds are',thresholds)

因此，如果它们是概率值，它们怎么不在0到1的范围内？这些决策函数值还是其他值？

Answer 1

precision_recall_curve为您提供特定阈值下的二进制分类器的精度和召回率值。假设您正在查看某个类的概率。拟合后，您可以通过predict_proba(self, X)函数获得概率。每个类一个概率。对于二进制分类器，这当然是两个类。这与predict(self, X)相反，后者实际上会让您知道某类的概率是否为> 0.5，然后返回该类。我猜您想做的是以理想的方式选择此阈值（默认为0.5）来优化f得分，召回率或精度。这可以通过使用上面提到的precision_recall_curve函数来实现。

以下示例显示了它的完成方式。

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.metrics import precision_recall_curve

X, y = load_iris(return_X_y=True)
# reduce multiclass to binary problem, i.e. class 0 or 1 (class 2 starts at index 100)
X = X[0:100]
y = y[0:100]

lr = LogisticRegression(random_state=0).fit(X, y)

y_test_hat = lr.predict_proba(X)

# just look at probabilities for class 1
y_test_hat_class_1 = y_test_hat[:,1]

precisions, recalls, thresholds = precision_recall_curve(y, y_test_hat_class_1)
f_scores = np.nan_to_num((2 * precisions * recalls) / (precisions + recalls))

for p, r, f, t in zip(precisions, recalls, f_scores, thresholds):
    print('Using threshold={} as decision boundary, we reach '
          'precision={}, recall={}, and f-score={}'.format(t, p, r, f))

f_max_index = np.argmax(f_scores)
max_f_score = f_scores[f_max_index]
max_f_score_threshold = thresholds[f_max_index]

print('The threshold for the max f-score is {}'.format(max_f_score_threshold))

通往：

Using threshold=0.8628645363798557 as decision boundary, we reach precision=1.0, recall=1.0, and f-score=1.0
Using threshold=0.9218669507660147 as decision boundary, we reach precision=1.0, recall=0.98, and f-score=0.98989898989899
Using threshold=0.93066642297958 as decision boundary, we reach precision=1.0, recall=0.96, and f-score=0.9795918367346939
Using threshold=0.9332685743944795 as decision boundary, we reach precision=1.0, recall=0.94, and f-score=0.9690721649484536
Using threshold=0.9395382533408563 as decision boundary, we reach precision=1.0, recall=0.92, and f-score=0.9583333333333334
Using threshold=0.9640718757241656 as decision boundary, we reach precision=1.0, recall=0.9, and f-score=0.9473684210526316
Using threshold=0.9670374623286897 as decision boundary, we reach precision=1.0, recall=0.88, and f-score=0.9361702127659575
Using threshold=0.9687934720210198 as decision boundary, we reach precision=1.0, recall=0.86, and f-score=0.924731182795699
Using threshold=0.9726392263137621 as decision boundary, we reach precision=1.0, recall=0.84, and f-score=0.9130434782608696
Using threshold=0.973775627114333 as decision boundary, we reach precision=1.0, recall=0.82, and f-score=0.9010989010989011
Using threshold=0.9740474969329987 as decision boundary, we reach precision=1.0, recall=0.8, and f-score=0.888888888888889
Using threshold=0.9741603105458991 as decision boundary, we reach precision=1.0, recall=0.78, and f-score=0.8764044943820225
Using threshold=0.9747085542467909 as decision boundary, we reach precision=1.0, recall=0.76, and f-score=0.8636363636363636
Using threshold=0.974749494774799 as decision boundary, we reach precision=1.0, recall=0.74, and f-score=0.8505747126436781
Using threshold=0.9769993303678443 as decision boundary, we reach precision=1.0, recall=0.72, and f-score=0.8372093023255813
Using threshold=0.9770140294088295 as decision boundary, we reach precision=1.0, recall=0.7, and f-score=0.8235294117647058
Using threshold=0.9785921201646789 as decision boundary, we reach precision=1.0, recall=0.68, and f-score=0.8095238095238095
Using threshold=0.9786461690308931 as decision boundary, we reach precision=1.0, recall=0.66, and f-score=0.7951807228915663
Using threshold=0.9789411518223052 as decision boundary, we reach precision=1.0, recall=0.64, and f-score=0.7804878048780487
Using threshold=0.9796555988114017 as decision boundary, we reach precision=1.0, recall=0.62, and f-score=0.7654320987654321
Using threshold=0.9801649093623934 as decision boundary, we reach precision=1.0, recall=0.6, and f-score=0.7499999999999999
Using threshold=0.9805566289582609 as decision boundary, we reach precision=1.0, recall=0.58, and f-score=0.7341772151898733
Using threshold=0.9808560894443067 as decision boundary, we reach precision=1.0, recall=0.56, and f-score=0.717948717948718
Using threshold=0.982400866419342 as decision boundary, we reach precision=1.0, recall=0.54, and f-score=0.7012987012987013
Using threshold=0.9828790909959155 as decision boundary, we reach precision=1.0, recall=0.52, and f-score=0.6842105263157895
Using threshold=0.9828854909335458 as decision boundary, we reach precision=1.0, recall=0.5, and f-score=0.6666666666666666
Using threshold=0.9839851081942663 as decision boundary, we reach precision=1.0, recall=0.48, and f-score=0.6486486486486487
Using threshold=0.9845312460821358 as decision boundary, we reach precision=1.0, recall=0.46, and f-score=0.6301369863013699
Using threshold=0.9857012993403023 as decision boundary, we reach precision=1.0, recall=0.44, and f-score=0.6111111111111112
Using threshold=0.9879940756602601 as decision boundary, we reach precision=1.0, recall=0.42, and f-score=0.5915492957746479
Using threshold=0.9882223190984861 as decision boundary, we reach precision=1.0, recall=0.4, and f-score=0.5714285714285715
Using threshold=0.9889482842475497 as decision boundary, we reach precision=1.0, recall=0.38, and f-score=0.5507246376811594
Using threshold=0.9892545856218082 as decision boundary, we reach precision=1.0, recall=0.36, and f-score=0.5294117647058824
Using threshold=0.9899303560728386 as decision boundary, we reach precision=1.0, recall=0.34, and f-score=0.5074626865671642
Using threshold=0.9905455482163618 as decision boundary, we reach precision=1.0, recall=0.32, and f-score=0.48484848484848486
Using threshold=0.9907019104721698 as decision boundary, we reach precision=1.0, recall=0.3, and f-score=0.4615384615384615
Using threshold=0.9911493537429485 as decision boundary, we reach precision=1.0, recall=0.28, and f-score=0.43750000000000006
Using threshold=0.9914230947944308 as decision boundary, we reach precision=1.0, recall=0.26, and f-score=0.41269841269841273
Using threshold=0.9915673581329265 as decision boundary, we reach precision=1.0, recall=0.24, and f-score=0.3870967741935484
Using threshold=0.9919835313724615 as decision boundary, we reach precision=1.0, recall=0.22, and f-score=0.36065573770491804
Using threshold=0.9925274516087134 as decision boundary, we reach precision=1.0, recall=0.2, and f-score=0.33333333333333337
Using threshold=0.9926276253093826 as decision boundary, we reach precision=1.0, recall=0.18, and f-score=0.3050847457627119
Using threshold=0.9930234956465036 as decision boundary, we reach precision=1.0, recall=0.16, and f-score=0.2758620689655173
Using threshold=0.9931758599517743 as decision boundary, we reach precision=1.0, recall=0.14, and f-score=0.24561403508771928
Using threshold=0.9935881899997894 as decision boundary, we reach precision=1.0, recall=0.12, and f-score=0.21428571428571425
Using threshold=0.9946684285206863 as decision boundary, we reach precision=1.0, recall=0.1, and f-score=0.18181818181818182
Using threshold=0.9960976336416663 as decision boundary, we reach precision=1.0, recall=0.08, and f-score=0.14814814814814814
Using threshold=0.996289803123931 as decision boundary, we reach precision=1.0, recall=0.06, and f-score=0.11320754716981131
Using threshold=0.9975518299472802 as decision boundary, we reach precision=1.0, recall=0.04, and f-score=0.07692307692307693
Using threshold=0.998322588642525 as decision boundary, we reach precision=1.0, recall=0.02, and f-score=0.0392156862745098
The threshold for the max f-score is 0.8628645363798557

此示例还计算了用于最大化f分数的阈值。

有关decision_function的更多信息，请参阅this answer的统计信息。

二进制分类器中的阈值

1 个答案: