Recall from lecture that irrelevant features can cause problems with computation, interpretation and even statistical performance.The following table shows three features (f1, f2, f3) in columns, for a small dataset with four instances in rows.Which features are irrelevant, or in other words, can be written as a linear combination of other features? (For each feature specify if it is irrelevant "yes", or not "no".)

Which of the following is not an indicator of irrelevant features?Group of answer choicesModel performance does not increase after adding the feature.The normalized feature importance is below 1%.The feature has a low correlation with the label.The feature was proposed by a domain expert on the problem.

Which is true of joint sufficiency?a.)Features which together are sufficient for an event to occurb.)Features which are necessary for an event to occurc.)A single feature which is sufficient for an event's occurrenced.)Features which are irrelevant to an event's occurrence

How does the inclusion of irrelevant variables in a regression model affect it?It increases the R-squared value.It reduces the standard error of estimate.It can lead to overfitting and reduced generalizability.It ensures a perfect fit to the data

Question 4Which one of the below terms use a linear combination of features?1 pointBinomial RegressionLinear RegressionMultiple RegressionPolynomial Regression

Hiding the implementation complexity can:Select one:a.Provide better featuresb.Make the programming complexc.Make the programming easyd.Provide more number of features