How is feature selection performed using the regularization method? – InterviewSolution

InterviewSolution

1.	How is feature selection performed using the regularization method?
Answer» The method of regularization ENTAILS the addition of PENALTIES to different parameters in the machine learning model for reducing the freedom of the model to avoid the issue of overfitting. There are various regularization methods available such as linear model regularization, Lasso/L1 regularization, etc. The linear model regularization applies penalty over COEFFICIENTS that multiplies the predictors. The Lasso/L1 regularization has the feature of shrinking some coefficients to zero, thereby making it ELIGIBLE to be removed from the model.

Discussion

No Comment Found

Related InterviewSolutions

How is the grid search parameter different from the random search tuning strategy?
What is the importance of dimensionality reduction?
How do you identify if a coin is biased?
How is feature selection performed using the regularization method?
What are various assumptions used in linear regression? What would happen if they are violated?
Is it good to do dimensionality reduction before fitting a Support Vector Model?
Give one example where both false positives and false negatives are important equally?
What are some examples when false positive has proven important than false negative?
Toss the selected coin 10 times from a jar of 1000 coins. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Estimate the probability of getting a head in the next coin toss.
Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. Evaluate the probability of finding at least one shooting star in a one-hour duration?