如何绘制多类分类器的精度和召回率? [英] How to plot precision and recall of multiclass classifier?

问题描述

我正在使用 scikit learn，我想绘制精度和召回曲线.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?

另外，我只找到了多标签的 SVM，它有一个 decision_function 而 RandomForest 没有

解决方案

来自 scikit-learn 文档:

• 3.ROC曲线

  # roc 曲线fpr = dict()tpr = dict()对于 i 在范围内(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(假阳性率")plt.ylabel(真阳性率")plt.legend(loc=最佳")plt.title(ROC 曲线")plt.show()

  I'm using scikit learn, and I want to plot the precision and recall curves. the classifier I'm using is RandomForestClassifier. All the resources in the documentations of scikit learn uses binary classification. Also, can I plot a ROC curve for multiclass?

  Also, I only found for SVM for multilabel and it has a decision_function which RandomForest doesn't have

  解决方案

  From scikit-learn documentation:

  • 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).

  Therefore, you should binarize the output and consider precision-recall and roc curves for each class. Moreover, you are going to use predict_proba to get class probabilities.

  I divide the code into three parts:

  1. general settings, learning and prediction
  2. precision-recall curve
  3. ROC curve

  1. general settings, learning and prediction

  from sklearn.datasets import fetch_mldata
  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_mldata("MNIST original")
  n_classes = len(set(mnist.target))
  
  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 curve

  # 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 curve

  # 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()
  

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