如何构建混淆矩阵？

Question

我有以下代码，它绘制了KNN算法的嵌套与非嵌套交叉验证。# Number of random trialsNUM_TRIALS = 30# Load the datasetX_iris = X.valuesy_iris = y# Set up possible values of parameters to optimize overp_grid = {"n_neighbors": [1, 5, 10]}# We will use a Support Vector Classifier with "rbf" kernelsvm = KNeighborsClassifier()# Arrays to store scoresnon_nested_scores = np.zeros(NUM_TRIALS)nested_scores = np.zeros(NUM_TRIALS)# Loop for each trialfor i in range(NUM_TRIALS):    # Choose cross-validation techniques for the inner and outer loops,    # independently of the dataset.    # E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc.    inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)    outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)    # Non_nested parameter search and scoring    clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)    clf.fit(X_iris, y_iris)    non_nested_scores[i] = clf.best_score_    # Nested CV with parameter optimization    nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)    nested_scores[i] = nested_score.mean()score_difference = non_nested_scores - nested_scorespreds=clf.best_estimator_.predict(X_test)from sklearn.metrics import confusion_matrixcm = confusion_matrix(y_test, preds)one, two, three, four,five,six,seven,eight,nine = confusion_matrix(y_test, preds).ravel()我遇到的问题是混淆矩阵绘图，我遇到了以下错误：ValueError                                Traceback (most recent call last)<ipython-input-22-13536688e18b> in <module>()     45 from sklearn.metrics import confusion_matrix     46 cm = confusion_matrix(y_test, preds)---> 47 one, two, three, four,five,six,seven,eight,nine = confusion_matrix(y_test, preds).ravel()     48 cm = [[one,two],[three,four],[five,six],[seven,eight],[nine,eight]]     49 ax= plt.subplot()ValueError: too many values to unpack (expected 9)我不知道如何解决这个问题。我的数据集中有 9 个目标变量，存储在 y 中。[11 11 11 ... 33 33 33] #the target variables being : 11,12,13,21,22,23,31,32,33

Answer 1

混淆矩阵由“cm = confusion_matrix（y_test，preds）”构建，其中cm是9x9矩阵（因为目标变量中有9个不同的标签）。如果要绘制它，可以使用plot_confusion_matrix函数。没有必要把它弄得乱七八糟。如果对其进行处理，则 9x9 矩阵将转换为 81 个值，并且您将它解压缩为赋值左侧的 9 个变量。这就是您收到“太多值无法解压缩（预期9）”错误的原因。