Problem statementSend feedbackSuppose 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
Problem statementSend feedbackSuppose 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 could potentially improve the model's ability to predict the target variable, thus increasing or at least maintaining the training accuracy.
However, it's important to note that this is not always the case. Adding irrelevant or redundant features could lead to overfitting, where the model performs well on the training data but poorly on unseen data (testing data). This is why feature selection and understanding the domain knowledge is crucial in machine learning.
As for the testing accuracy, it could also increase or remain the same after adding new features for the same reasons. But again, this is not guaranteed as it depends on whether the new features actually improve the model's predictive power without causing overfitting.
So, while the training accuracy could potentially always increase or remain the same, and the testing accuracy could potentially always increase or remain the same, these are not hard and fast rules and depend on the specific features and model.
Similar Questions
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
Multiple Select QuestionSelect all that applyWhich of the following statements about performance feedback are true?Multiple select question.It is important, even if it is negative.It can be counterproductive if you react to it improperly.It helps you improve your skills.It tells you little about how to improve your skills.
Select all that applyWhat are formal training options for additional skills and training?Multiple select question.professional organizationsonline sourcescompany programstrial by error
1.Question 1Which option lists the steps of training a logistic regression model in the correct order?Use the cost function on the training set.Update weights with new parameter values.Calculate cost function gradient.Initialize the parameters.Repeat until specified cost or iterations reached.1 point4, 1, 3, 2, 5 1, 4, 3, 2, 53, 2, 5, 4, 14, 3, 2, 5, 1
Select all options that apply.A requirement should state:Group of answer choicesWhy it should be doneHow good should it beThe conditions under which it needs to be performedWhat is needed
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.