我是机器学习的新手。最近，我学会了如何计算KNN分类的测试集的confusion_matrix。但是，我不知道如何计算KNN分类的训练集的confusion_matrix？
我如何从以下代码中计算KNN分类的训练集的confusion_matrix？
以下代码用于计算测试集的confusion_matrix：
# Split test and train dataimport numpy as npfrom sklearn.model_selection import train_test_splitX = np.array(dataset.ix[:, 1:10])y = np.array(dataset['benign_malignant'])X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)#Define Classifierfrom sklearn.neighbors import KNeighborsClassifierknn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)knn.fit(X_train, y_train)# Predicting the Test set resultsy_pred = knn.predict(X_test)# Making the Confusion Matrixfrom sklearn.metrics import confusion_matrixcm = confusion_matrix(y_test, y_pred) # Calulate Confusion matrix for test set.关于k折交叉验证：
我也在尝试使用k折交叉验证来查找训练集的confusion_matrix。
我对这一行knn.fit(X_train, y_train)感到困惑。
我是否需要更改这一行knn.fit(X_train, y_train)？
我应该在以下代码的哪里进行更改以计算训练集的confusion_matrix？
# Applying k-fold Methodfrom sklearn.cross_validation import StratifiedKFoldkfold = 10 # no. of folds (better to have this at the start of the code)skf = StratifiedKFold(y, kfold, random_state = 0)# Stratified KFold: This first divides the data into k folds. Then it also makes sure that the distribution of the data in each fold follows the original input distribution # Note: in future versions of scikit.learn, this module will be fused with kfoldskfind = [None]*len(skf) # indicescnt=0for train_index in skf: skfind[cnt] = train_index cnt = cnt + 1# skfind[i][0] -> train indices, skfind[i][1] -> test indices# Supervised Classification with k-fold Cross Validationfrom sklearn.metrics import confusion_matrixfrom sklearn.neighbors import KNeighborsClassifierconf_mat = np.zeros((2,2)) # Initializing the Confusion Matrixn_neighbors = 1; # better to have this at the start of the code# 10-fold Cross Validationfor i in range(kfold): train_indices = skfind[i][0] test_indices = skfind[i][1] clf = [] clf = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2) X_train = X[train_indices] y_train = y[train_indices] X_test = X[test_indices] y_test = y[test_indices] # fit Training set clf.fit(X_train,y_train) # predict Test data y_predcit_test = [] y_predict_test = clf.predict(X_test) # output is labels and not indices # Compute confusion matrix cm = [] cm = confusion_matrix(y_test,y_predict_test) print(cm) # conf_mat = conf_mat + cm 回答：
你不需要做太多的更改
# Predicting the train set resultsy_train_pred = knn.predict(X_train)cm_train = confusion_matrix(y_train, y_train_pred)这里我们使用X_train进行分类，而不是使用X_test，然后我们使用训练数据集的预测类别和实际类别生成分类矩阵。
分类矩阵的基本思想是找出分类落入四个类别中的数量（如果y是二元的）：
- 预测为真但实际为假
- 预测为真且实际为真
- 预测为假但实际为真
- 预测为假且实际为假
只要你有两个集合——预测和实际，你就可以创建混淆矩阵。你所需要做的就是预测类别，并使用实际类别来获得混淆矩阵。
编辑
在交叉验证部分，你可以添加一行y_predict_train = clf.predict(X_train)来计算每次迭代的混淆矩阵。你可以这样做，因为在循环中，你每次都初始化clf，这基本上意味着重置你的模型。
另外，在你的代码中，你每次都计算混淆矩阵，但你没有将它存储在任何地方。最后你只会得到最后一个测试集的cm。
