将缩放器，PCA和随机森林管道应用于3D阵列
我有一个用于x_train和x_test的3D数组。是否可以使用sualer，pca和随机森林管道到3D数组？但是，我尝试使用以下代码，但是，我会收到错误：
valueerror：dim 3。标准标准预期＆lt; = 2。，
如果可能，我如何正确使用下面的管道来正确地使用下面的管道3D阵列？
这是我的代码：
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
pipe = Pipeline([('scaler', StandardScaler()),
('pca', PCA(n_components=15)),
('clf', RandomForestClassifier())])
# Declare a hyperparameter grid
parameter_space = {
'clf__n_estimators': [10,50,100],
'clf__criterion': ['gini', 'entropy'],
'clf__max_depth': np.linspace(10,50,11),
}
clf = GridSearchCV(pipe, parameter_space, cv = 5, scoring = "accuracy", verbose = True) # model
pipe.fit(X_train,y_train)
I have a 3D array for X_train and X_test. Is it possible to use a scaler, PCA and Random Forest pipeline to the 3D array? I tried using the code below, however, I get the error:
ValueError: Found array with dim 3. StandardScaler expected <= 2.
If possible, how do I go about correctly using the pipeline below for the 3D array?
Here is my code:
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
pipe = Pipeline([('scaler', StandardScaler()),
('pca', PCA(n_components=15)),
('clf', RandomForestClassifier())])
# Declare a hyperparameter grid
parameter_space = {
'clf__n_estimators': [10,50,100],
'clf__criterion': ['gini', 'entropy'],
'clf__max_depth': np.linspace(10,50,11),
}
clf = GridSearchCV(pipe, parameter_space, cv = 5, scoring = "accuracy", verbose = True) # model
pipe.fit(X_train,y_train)
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