Python sklearn随机森林相互覆盖

我使用sklearn进行随机森林分类。现在我想比较不同的描述符集（一个有125个特性，一个有154个特性）。因此，我创建了两个不同的随机林，但它们似乎相互覆盖，从而导致错误： '模型的特征数量必须与输入匹配。型号n_特征为125，输入n_特征为154'

rf_std = RandomForestClassifier(n_estimators = 150, max_depth = 200, max_features = 'sqrt')
rf_nostd = RandomForestClassifier(n_estimators = 150, max_depth = 200, max_features = 'sqrt')

rf_std=rf_std.fit(X_train_std,y_train_std)
print('Testing score std:',rf_std.score(X_test_std,y_test_std))

rf_nostd=rf_nostd.fit(X_train_nostd,y_train_nostd)
print('Testing score nostd:',rf_nostd.score(X_test_nostd,y_test_nostd))
# until here it works

fig, (ax1, ax2) = plt.subplots(1, 2)

disp = plot_confusion_matrix(rf_std, X_test_std, y_test_std,
                                 cmap=plt.cm.Blues,
                                 normalize='true',ax=ax1)
disp = plot_confusion_matrix(rf_nostd, X_test_nostd, y_test_nostd,
                                 cmap=plt.cm.Blues,
                                 normalize='true',ax=ax2)
plt.show()
#here i get the error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-27-eee9fea5dbfb> in <module>
      3 disp = plot_confusion_matrix(rf_std, X_test_std, y_test_std,
      4                                  cmap=plt.cm.Blues,
----> 5                                  normalize='true',ax=ax1)
      6 disp = plot_confusion_matrix(rf_nostd, X_test_nostd, y_test_nostd,
      7                                  cmap=plt.cm.Blues,

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_plot\confusion_matrix.py in plot_confusion_matrix(estimator, X, y_true, labels, sample_weight, normalize, display_labels, include_values, xticks_rotation, values_format, cmap, ax)
    183         raise ValueError("plot_confusion_matrix only supports classifiers")
    184 
--> 185     y_pred = estimator.predict(X)
    186     cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
    187                           labels=labels, normalize=normalize)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in predict(self, X)
    610             The predicted classes.
    611         """
--> 612         proba = self.predict_proba(X)
    613 
    614         if self.n_outputs_ == 1:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in predict_proba(self, X)
    654         check_is_fitted(self)
    655         # Check data
--> 656         X = self._validate_X_predict(X)
    657 
    658         # Assign chunk of trees to jobs

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in _validate_X_predict(self, X)
    410         check_is_fitted(self)
    411 
--> 412         return self.estimators_[0]._validate_X_predict(X, check_input=True)
    413 
    414     @property

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py in _validate_X_predict(self, X, check_input)
    389                              "match the input. Model n_features is %s and "
    390                              "input n_features is %s "
--> 391                              % (self.n_features_, n_features))
    392 
    393         return X

ValueError: Number of features of the model must match the input. Model n_features is 125 and input n_features is 154 

---

为什么这两个模型没有分开，有人能帮忙吗？

我能够用不一致的

train

和

test

输入形状重现这个错误

试试这个：

assert X_train_std.shape[-1] == X_test_std.shape[-1], "Input shapes don't match."
assert X_train_nostd.shape[-1] == X_test_nostd.shape[-1], "Input shapes don't match."

我就是这样再现你的错误的：

import numpy as np
from sklearn.ensemble import RandomForestClassifier

X_train_std = np.random.rand(400, 154)
X_test_std = np.random.rand(100, 125)

y_train_std = np.random.randint(0, 2, 400).tolist()
y_test_std = np.random.randint(0, 2, 100).tolist()

rf_std = RandomForestClassifier(n_estimators = 150, 
    max_depth = 200, max_features = 'sqrt')

rf_std=rf_std.fit(X_train_std,y_train_std)
print('Testing score std:',rf_std.score(X_test_std,y_test_std))

ValueError：模型的特征数必须与输入匹配。型号n_特征为154，输入n_特征为125

---

这通常发生在训练集/测试集与形状不匹配时。 请检查以下形状信息是否匹配

X_train_std.shape[1] == X_test_std.shape[1]  
X_train_nostd.shape[1] == X_test_nostd.shape[1]

如果它匹配，你就可以很好地使用它，否则你就必须寻找你发现差异的地方

问候，

---

MJ

我正试图重现你的问题。你的输入形状是什么？你的错误是什么？？你能通过编辑在帖子上显示它吗？X_train_std是一个np数组（40000154）y_train_std一个列表（40000），X_train_nostd是一个np数组（40000125），y_train_nostd一个列表（40000）。std和NOSD测试集的尺寸分别为（10000154）和（10000125）。谢谢你的回答，我确实把输入搞乱了。

X_train_std.shape[1] == X_test_std.shape[1]  
X_train_nostd.shape[1] == X_test_nostd.shape[1]