Hello,
I'm using scikit-learn for machine learning. I have 800 samples with 2048 features, therefore I want to reduce my features to get hopefully a better accuracy.
It is a multiclass problem (class 0-5), and the features consists of 1's and 0's: [1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0....,0]
I'm using the Random Forest Classifier.
Should I just feature select the training data ? And is it enough if I'm using this code:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)
clf=RandomForestClassifier(n_estimators=200,warm_start=True,criterion='gini', max_depth=13)
clf.fit(X_train, y_train).transform(X_train)
predicted=clf.predict(X_test)
expected=y_test
confusionMatrix=metrics.confusion_matrix(expected,predicted)
Cause the accuracy didn't get higher. Is everything OK in the code or am I doing something wrong?
I'll be very grateful for your help.
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written
by
hrbrt.sch •
1 answer
Should I just feature select the training data?
Yes, it is just for training set. After some important features was picked up based on the training set, the you can use these features in the test set.
For the accuracy, there are many factors can give an effect on it. For example, normalized features and imbalanced samples, etc.
Hope this helps
Kevin
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learnBioinformatics •
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