sklearn.metrics.confusion_matrix用法

混淆矩阵

sklearn.metrics.confusion_matrix(y_true, 
                                 y_pred, 
                                 labels=None, 
                                 sample_weight=None
                            )

参数：

• y_true: array, shape = [n_samples] 是样本真实分类结果，
• y_pred: array, shape = [n_samples] 是样本预测分类结果
• labels：array, shape = [n_classes], optional 是所给出的类别，通过这个可对类别进行选择, 少数类在前，多数类在后
• sample_weight: 样本权重

返回值：

• C : array, shape = [n_classes, n_classes] Confusion matrix

coding

>>> from sklearn.metrics import confusion_matrix
# 三分类的情况
>>> y_true = [2, 1, 0, 1, 2, 0]
>>> y_pred = [2, 0, 0, 1, 2, 1]
>>> confusion_matrix(y_true, y_pred)
array([[1, 1, 0],
       [1, 1, 0],
       [0, 0, 2]], dtype=int64)

1. 自建数据集

   class_1_ = 7
   class_2_ = 4
   centers_ = [[0.0, 0.0], [1,1]]
   clusters_std = [0.5, 1]
   X_, y_ = make_blobs(n_samples=[class_1_, class_2_],
                       centers=centers_,
                       cluster_std=clusters_std,
                       random_state=0, shuffle=False)
   
   plt.scatter(X_[:, 0], X_[:, 1], c=y_, cmap="rainbow",s=30)
   

   

2. 建模，调用概率

   from sklearn.linear_model import LogisticRegression as LogiR
   
   clf_lo = LogiR().fit(X_,y_)
   prob = clf_lo.predict_proba(X_)
   
   #将样本和概率放到一个DataFrame中
   import pandas as pd
   prob = pd.DataFrame(prob)
   prob.columns = ["0","1"]
   

3. 使用阈值0.5，大于0.5的样本被预测为1，小于0.5的样本被预测为0

   #手动调节阈值，来改变我们的模型效果
   for i in range(prob.shape[0]):
       if prob.loc[i,"1"] > 0.5:
           # 添加新的标签列
           prob.loc[i,"pred"] = 1
       else:
           # 添加新的标签列
           prob.loc[i,"pred"] = 0
   
   # 添加真实的标签列
   prob["y_true"] = y_
   prob = prob.sort_values(by="1",ascending=False)
   

4. 使用混淆矩阵查看结果

   from sklearn.metrics import confusion_matrix as CM
   
   CM(prob.loc[:,"y_true"],prob.loc[:,"pred"],labels=[1,0])
   
   #array([[2, 2],
   #       [1, 6]], dtype=int64)