随机森林和逻辑回归显示准确率为99%

Question

我有50000次观察，其中70%为正目标变量，30%为负目标变量 我得到了96-99%左右的准确率，这似乎是不真实的，当然，我担心我的模型过于合适，我不明白为什么。我用第5和第95个分位数替换了所有的异常值。标准化的数据，但它显示出这种不真实的准确性。 通过一些在线搜索，人们建议检查训练和测试数据准确性之间的差异，我做到了这一点，而随机森林也做到了这一点

Training Accuracy: 0.997975
Test Accuracy: 0.9715

逻辑回归显示

Training Accuracy: 0.967225
Test Accuracy: 0.9647

这是我用于运行模型的代码：

clf = LogisticRegression()
trained_model = clf.fit(X_train, y_train)
trained_model.fit(X_train, y_train)
predictions = trained_model.predict(X_test)      

accuracy_score(y_train, trained_model.predict(X_train))
accuracy_score(y_test, predictions)

我还尝试了kfold交叉验证，得到了类似的结果

skfold = StratifiedKFold(n_splits=10, random_state=100)
model_skfold = LogisticRegression()
results_skfold = model_selection.cross_val_score(model_skfold, X, Y, cv=skfold)
print("Accuracy: %.2f%%" % (results_skfold.mean()*100.0))

最后，我应用正则化技术来检查结果，这就是我得到的结果

for c in C:
    clf = LogisticRegression(penalty='l1', C=c, solver='liblinear')
    clf.fit(X_train, y_train)
    y_pred_log_reg = clf.predict(X_test)
    acc_log_reg = round( clf.score(X_train, y_train) * 100, 2)
    print (str(acc_log_reg) + ' percent')
    print('C:', c)
    print('Coefficient of each feature:', clf.coef_)
    print('Training accuracy:', clf.score(X_train_std, y_train))
    print('Test accuracy:', clf.score(X_test_std, y_test))
    print('')

结果

96.72 percent
C: 10
Coefficient of each feature: [[-2.50e+00 -1.40e-03  2.65e+00  4.09e-02 -2.03e-03  2.75e-04  1.79e-02
  -2.13e-03 -2.18e-03  2.90e-03  2.69e-03 -4.93e+00 -4.89e+00 -4.88e+00
  -3.27e+00 -3.30e+00]]
Training accuracy: 0.5062
Test accuracy: 0.5027

96.72 percent
C: 1
Coefficient of each feature: [[-2.50e+00 -1.41e-03  2.66e+00  4.10e-02 -2.04e-03  2.39e-04  1.68e-02
  -3.29e-03 -3.80e-03  2.52e-03  2.62e-03 -4.22e-02 -9.55e-03  0.00e+00
  -1.73e+00 -1.77e+00]]
Training accuracy: 0.482525
Test accuracy: 0.4738

96.74 percent
C: 0.1
Coefficient of each feature: [[-2.46e+00 -1.38e-03  2.58e+00  4.03e-02 -1.99e-03  2.22e-04  1.44e-02
  -4.49e-03 -5.13e-03  2.03e-03  2.20e-03  0.00e+00  0.00e+00  0.00e+00
   0.00e+00 -6.54e-03]]
Training accuracy: 0.616675
Test accuracy: 0.6171

95.92 percent
C: 0.001
Coefficient of each feature: [[-1.43e+00 -6.82e-04  1.19e+00  2.73e-02 -1.10e-03  1.22e-04  0.00e+00
  -2.74e-03 -2.55e-03  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00
   0.00e+00  0.00e+00]]
Training accuracy: 0.655075
Test accuracy: 0.6565

我用于标准化和替换异常值的代码

std_scale = preprocessing.StandardScaler().fit(X_train)
X_train_std = std_scale.transform(X_train)
X_test_std  = std_scale.transform(X_test)

X.clip(lower=X.quantile(0.05), upper=X.quantile(0.95), axis = 1, inplace = True)

如果需要任何其他信息，请务必告诉我，我们将不胜感激

编辑：

热图