我有一组数据，每列中的数字行是输入，字母是输出。
A,A,A,B,B,B-0.979090189,0.338819904,-0.253746508,0.213454999,-0.580601104,-0.441683968-0.48395313,0.436456904,-1.427424032,-0.107093825,0.320813402,0.060866105-1.098818173,-0.999161692,-1.371721698,-1.057324962,-1.161752652,-0.854872591-1.53191442,-1.465454248,-1.350414216,-1.732518018,-1.674040715,-1.5615684962.522796162,2.498153298,3.11756171,2.125738509,3.003929536,2.514411247-0.060161596,-0.487513844,-1.083513761,-0.908023322,-1.047536921,-0.482767590.241962669,0.181365373,0.174042637,-0.048013217,-0.177434916,0.42738621-0.603856395,-1.020531402,-1.091134021,-0.863008165,-0.683233589,-0.849059931-0.626159165,-0.348144322,-0.518640038,-0.394482485,-0.249935646,-0.543947259-1.407263942,-1.387660115,-1.612988118,-1.141282747,-0.944745366,-1.030944216-0.682567673,-0.043613473,-0.105679403,0.135431139,0.059104888,-0.132060832-1.10107164,-1.030047313,-1.239075022,-0.651818656,-1.043589073,-0.765992541我尝试使用KNN留一法交叉验证（LOOCV）来获取准确率评分和混淆矩阵。
from sklearn.neighbors import KNeighborsClassifierfrom sklearn.metrics import accuracy_scorefrom sklearn.metrics import confusion_matrixfrom sklearn.model_selection import LeaveOneOutimport pandas as pddef main(): csv = 'data.csv' df = pd.read_csv(csv) X = df.values.T y = df.columns.values clf = KNeighborsClassifier() loo = LeaveOneOut() for train_index, test_index in loo.split(X): X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] clf.fit(X_train, y_train) y_true = y_test y_pred = clf.predict(X_test) ac = accuracy_score(y_true, y_pred) cm = confusion_matrix(y_true, y_pred) print ac print cmif __name__ == '__main__': main()然而，我的结果都是0。我哪里做错了？
回答：
我认为你的模型没有正确训练，因为它只需要猜测一个值，所以猜不对。我建议你改用KFold或StratifiedKFold。留一法交叉验证（LOO）对于大样本来说非常耗时。以下是我在你的X数据上使用3折分层的StratifiedKFold的结果。我随机用0和1填充了y，而不是使用A和B，并且没有转置数据，所以它有12行：
from sklearn.neighbors import KNeighborsClassifierfrom sklearn.metrics import accuracy_scorefrom sklearn.metrics import confusion_matrixfrom sklearn.model_selection import StratifiedKFoldimport pandas as pdcsv = 'C:\df_low_X.csv'df = pd.read_csv(csv, header=None)print(df)X = df.iloc[:, :-1].valuesy = df.iloc[:, -1].valuesclf = KNeighborsClassifier()kf = StratifiedKFold(n_splits = 3)ac = []cm = []for train_index, test_index in kf.split(X,y): X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] print(X_train, X_test) clf.fit(X_train, y_train) y_pred = clf.predict(X_test) ac.append(accuracy_score(y_test, y_pred)) cm.append(confusion_matrix(y_test, y_pred))print(ac)print(cm)# ac[0.25, 0.75, 0.5]# cm[array([[1, 1], [2, 0]], dtype=int64), array([[1, 1], [0, 2]], dtype=int64), array([[0, 2], [0, 2]], dtype=int64)]