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.

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 statement is true regarding Feature Selection?

Which of the following statement is true?  Select one:a.Functionality is not important, even Quality User Experience is also not importantb.Functionality is important, but Quality User Experience is also important c. Functionality is important, but Quality User Experience is not important d.Functionality is not important, but Quality User Experience is important

If the features of Model 1 are a strict subset of those in Model 2. which model will USUALLY have lower TEST error?Group of answer choicesModel 1It's impossible to tell with only this informationModel 2

Consider the code snippet that extracts and prints the feature importances from a trained random forest regressor model. The model is used to predict Standard_yield based on various features. The code utilises the feature_importances_ attribute of the random forest model to obtain importance scores for each feature.Which of the following statements best describes the purpose and outcome of the provided code snippet?OptionsThe code identifies and prints the importance scores for each feature in the random forest model, indicating how much each feature contributes to the model's ability to predict Standard_yield. Higher scores suggest a greater contribution to the prediction.The code counts the number of times each feature is used to split the data across all trees in the random forest, thereby determining each feature's importance in predicting Standard_yield.The code calculates and prints the coefficient values for each feature used in the random forest, model to predict Standard_yield, indicating the strength and direction of the relationship between each feature and the target variable.The code computes the correlation between each feature and the target variable Standard_yield, printing a list of features sorted by their correlation coefficients to identify the most relevant predictors.