如何绘制多类分类器的精度和召回率？

Question

我正在使用 scikit learn，我想绘制准确率和召回率曲线。我使用的分类器是 RandomForestClassifier。 scikit learn 文档中的所有资源都使用二进制分类。另外，我可以为多类绘制 ROC 曲线吗？

此外，我只找到了用于多标签的 SVM，它有一个 decision_function 而 RandomForest 没有

Answer 1

来自 scikit-learn 文档：

Precision-Recall:

Precision-recall curves are typically used in binary classification to study the output of a classifier. In order to extend the precision-recall curve and average precision to multi-class or multi-label classification, it is necessary to binarize the output. One curve can be drawn per label, but one can also draw a precision-recall curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging).

Receiver Operating Characteristic (ROC):

ROC curves are typically used in binary classification to study the output of a classifier. In order to extend ROC curve and ROC area to multi-class or multi-label classification, it is necessary to binarize the output. One ROC curve can be drawn per label, but one can also draw a ROC curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging).

因此，您应该将输出二值化并考虑每个 class 的 precision-recall 和 roc 曲线。此外，您将使用 predict_proba 来获得 class 概率。

我把代码分成三部分：

一般设置、学习和预测
precision-recall曲线
ROC曲线

1.一般设置、学习和预测

from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import precision_recall_curve, roc_curve
from sklearn.preprocessing import label_binarize

import matplotlib.pyplot as plt
#%matplotlib inline

mnist = fetch_openml("mnist_784")
y = mnist.target
y = y.astype(np.uint8)
n_classes = len(set(y))

Y = label_binarize(mnist.target, classes=[*range(n_classes)])

X_train, X_test, y_train, y_test = train_test_split(mnist.data,
                                                    Y,
                                                    random_state = 42)

clf = OneVsRestClassifier(RandomForestClassifier(n_estimators=50,
                             max_depth=3,
                             random_state=0))
clf.fit(X_train, y_train)

y_score = clf.predict_proba(X_test)

2。 precision-recall曲线

# precision recall curve
precision = dict()
recall = dict()
for i in range(n_classes):
    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],
                                                        y_score[:, i])
    plt.plot(recall[i], precision[i], lw=2, label='class {}'.format(i))
    
plt.xlabel("recall")
plt.ylabel("precision")
plt.legend(loc="best")
plt.title("precision vs. recall curve")
plt.show()

3。 ROC曲线

# roc curve
fpr = dict()
tpr = dict()

for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i],
                                  y_score[:, i]))
    plt.plot(fpr[i], tpr[i], lw=2, label='class {}'.format(i))

plt.xlabel("false positive rate")
plt.ylabel("true positive rate")
plt.legend(loc="best")
plt.title("ROC curve")
plt.show()

如何绘制多类分类器的精度和召回率？

How to plot precision and recall of multiclass classifier?

python

matplotlib

roc

scikit-learn

precision-recall