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I am trying to learn more about Feature Selection in machine learning. I am working on a dataset that contains 17 features, and I have achieved about 75% accuracy on a Random Forest model with no parameters by manually selecting about 8 features using a correlation matrix by leaving those that has the same correlation score and select the features that I think should be used to train the model. I tried to implement RFE with Cross Validation, but it returns different feature every time I run the notebook. It also returns different features that I manually picked which I think is the reason why my model returns a lower accuracy score. My code is as follows:

from sklearn.feature_selection import RFECV
from sklearn.model_selection import StratifiedKFold

min_features_to_select = 1  # Minimum number of features to consider
clf = RandomForestClassifier(n_estimators=500, random_state=42) # I am using a RF Classifier
cv = StratifiedKFold(n_splits = 5, shuffle=False)

rfecv = RFECV(
    estimator=clf,
    step=1,
    cv=cv,
    scoring="accuracy",
    min_features_to_select=min_features_to_select,
    n_jobs=2,
)
rfecv.fit(X, y)

print(f"Optimal number of features: {rfecv.n_features_}")
print(f"Selected features are {X.columns[rfecv.support_]}")

I took this from the RFECV example in the sklearn documentation. I am really trying to figure out if there is a systematic way to pick features rather than just manually selecting them using correlation matrix.

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