Suppose I applied a logistic regression model on data and got training accuracy X and testing accuracy Y. Now I want to add few new features in data. Select option(s) which are correct in such case.Note: Consider remaining parameters are same.1. Training accuracy always decreases.2. Training accuracy always increases or remain same.3. Testing accuracy always decreases4. Testing accuracy always increases or remain same
Question
Suppose I applied a logistic regression model on data and got training accuracy X and testing accuracy Y. Now I want to add few new features in data. Select option(s) which are correct in such case.Note: Consider remaining parameters are same.1. Training accuracy always decreases.2. Training accuracy always increases or remain same.3. Testing accuracy always decreases4. Testing accuracy always increases or remain same
Solution
The correct options in this case would be:
- Training accuracy always increases or remains the same.
- Testing accuracy always increases or remains the same.
Explanation:
When you add new features to your data, your model has more information to learn from. This can potentially improve the model's ability to predict the target variable, which would increase the training accuracy or at least keep it the same.
However, it's important to note that this doesn't guarantee an improvement in testing accuracy. The testing accuracy could increase, decrease, or remain the same. It depends on whether the new features help the model generalize better to unseen data. If the new features only add noise or irrelevant information, they could even hurt the model's performance on the testing set. Therefore, it's always important to validate the model on a testing set or through cross-validation when adding new features.
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