因此，我对机器学习和Python还是一个新手，但是设法对我的数据进行分类，并使用以下代码通过各种分类器打印混淆矩阵：
def classify_data(df, feature_cols, file):
nbr_folds = 5
attributes = df.loc[:, feature_cols] # Also known as x
class_label = df['task'] # Class label, also known as y.
file.write("\nFeatures used: ")
for feature in feature_cols:
file.write(feature + ",")
print("Features used", feature_cols)
print("MLP")
file.write("MLP")
mlp = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
class_label_predicted = cross_val_predict(mlp, attributes, class_label, cv=nbr_folds)
conf_mat = confusion_matrix(class_label, class_label_predicted)
print(conf_mat)
accuracy = accuracy_score(class_label, class_label_predicted)
print("\nRows classified: " + str(len(class_label_predicted)))
print("\nAccuracy: {0:.3f}%\n".format(accuracy * 100))
file.write("\nClassifier settings:" + str(mlp) + "\n")
file.write("\nRows classified: " + str(len(class_label_predicted)))
file.write("\nAccuracy: {0:.3f}%\n".format(accuracy * 100))
file.writelines('\t'.join(str(j) for j in i) + '\n' for i in conf_mat)
print("RandomForest")
file.write("\nRandomForest")
#sv = svm.SVC(kernel="linear")
clf = RandomForestClassifier(max_depth=2, random_state=0)
class_label_predicted = cross_val_predict(clf, attributes, class_label, cv=nbr_folds)
conf_mat = confusion_matrix(class_label, class_label_predicted)
print(conf_mat)
accuracy = accuracy_score(class_label, class_label_predicted)
print("Rows classified: " + str(len(class_label_predicted)))
print("Accuracy: {0:.3f}%\n".format(accuracy * 100))
file.write("\nClassifier settings:" + str(clf) + "\n")
file.write("\nRows classified: " + str(len(class_label_predicted)))
file.write("\nAccuracy: {0:.3f}%\n".format(accuracy * 100))
file.writelines('\t'.join(str(j) for j in i) + '\n' for i in conf_mat)
但是，我开始怀疑我在这里是否做错了什么，因为混淆矩阵几乎总是一样，将所有内容都放在了我的第五个功能上。当我在Weka应用程序中使用相同的属性运行完全相同的数据集时，得到的结果是不同的。下面是一个示例：
sci kit learn:
MLP
Rows classified: 6881
Accuracy: 25.970%
0 0 0 0 412 12 0 0 25 1 0 0 0
0 0 0 0 540 50 0 0 8 0 0 0 0
0 0 0 0 111 3 0 0 6 2 0 0 0
0 0 0 0 139 19 0 0 4 2 0 0 0
0 0 0 0 1630 54 0 0 106 18 0 0 0
0 0 0 0 554 63 0 0 22 0 0 0 0
0 0 0 0 246 8 0 0 33 10 0 0 0
0 0 0 0 324 39 0 0 8 0 0 0 0
0 0 0 0 605 60 0 0 90 5 0 0 0
0 0 0 0 519 31 0 0 72 4 0 0 0
0 0 0 0 455 19 0 0 10 1 0 0 0
0 0 0 0 260 11 0 0 21 1 0 0 0
0 0 0 0 236 8 0 0 21 3 0 0 0
RandomForest:
Rows classified: 6881
Accuracy: 26.174%
0 0 0 0 440 0 0 0 10 0 0 0 0
0 0 0 0 597 0 0 0 0 1 0 0 0
0 0 0 0 119 0 0 0 3 0 0 0 0
0 0 0 0 164 0 0 0 0 0 0 0 0
0 0 0 0 1774 0 0 0 34 0 0 0 0
0 0 0 0 629 0 0 0 10 0 0 0 0
0 0 0 0 268 0 0 0 29 0 0 0 0
0 0 0 0 371 0 0 0 0 0 0 0 0
0 0 0 0 733 0 0 0 27 0 0 0 0
0 0 0 0 605 0 0 0 21 0 0 0 0
0 0 0 0 484 0 0 0 1 0 0 0 0
0 0 0 0 286 0 0 0 7 0 0 0 0
0 0 0 0 263 0 0 0 5 0 0 0 0
Weka
MLP
a b c d e f g h i j k l m <-- classified as
5 504 50 1 0 0 10 28 0 0 0 0 0 | a = t1
2 1511 56 1 4 1 83 135 0 2 12 0 1 | b = t12
4 467 88 0 1 3 30 45 0 0 0 1 0 | c = t2
1 227 15 2 2 0 36 13 0 1 0 0 0 | d = t3
4 369 18 2 1 0 25 31 0 0 0 0 0 | e = t0
3 306 43 0 1 2 10 6 0 0 0 0 0 | f = t4
5 463 36 2 4 0 178 69 0 0 2 0 1 | g = t5
3 371 23 1 0 0 49 176 0 0 2 1 0 | h = t6
4 398 14 1 1 0 28 33 0 0 5 1 0 | i = t7
1 252 13 0 0 0 16 8 0 1 2 0 0 | j = t8
1 213 9 0 0 0 20 24 0 1 0 0 0 | k = t9
1 96 3 0 0 0 4 16 0 0 2 0 0 | l = t10
1 133 7 0 0 0 7 15 0 0 1 0 0 | m = t11
我还想知道是否可以像Weka一样用类标签打印混淆矩阵？在这里，看来b列与sci kit Learn中的第五列相当，但是很难说出它代表的是哪一列。
最佳答案
看来您的数据集严重不平衡-第五类非常占主导地位，并且您的模型大部分时间只是在学习预测该标签。
怎么处理呢？阅读例如this。
关于python - 科学工具包学习混淆矩阵总是看起来几乎一样，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/50319858/