Knowledge check

Completed

1.

The tidymodels framework was used in R to train a regression model from a dataset of sales data. To evaluate the model to ensure it will predict accurately with new data, what should be done?

Split the data randomly into two subsets. Use one subset to train the model and the other subset to evaluate it.

Use all the data to train the model. Then use all the data to evaluate it.

Train the model by using only the feature columns. Then evaluate it by using only the label column.

2.

A regression model specification was created by using the linear_reg() function in the tidymodels parsnip package. What should be done to train the model?

Call the predict() function and specify the model specification, formula, and data.

Call the recipe() function and specify the model specification, formula, and data.

Call the fit() function and specify the model specification, formula, and data.

3.

A regression model was trained by using the tidymodels framework. When it's evaluated with test data, the model achieves an R-squared metric of 0.95. What does this metric say about the model?

The model is 95% accurate.

The model explains most of the variance between predicted and actual values.

On average, predictions are 0.95 higher than actual values.

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