7. Which among the following is a metric to measure a regression problem which has a characteristic of penalising large losses more than small losses?- i. mean squared error- ii. mean absolute error- iii. mean absolute percentage error- iv. accruracyi. onlyii. and iii. onlyiv. onlyall of the mentionediii. onlyii. only

Q.No.1. The _________ metric measures the squared difference between predicted and actualvalues in linear regression

The three common evaluation metrics for regression are

Question 6All of these options are useful error measures to compare regressions except:1 pointSSER squaredTSSROC index

2. You need to evaluate a classification model. Which metric can you use? ​​Mean squared error (MSE)PrecisionSilhouette

Match the definitions to the correct regression evaluation metrics:Tells us the proportion of variation in Y explained by X. Caution needed since it can be artificially inflated by adding too many input variablesThis is not a regression evaluation metricTells us the proportion of variation in Y explained by X. Additionally, it punishes the inclusion of useless input variables, and rewards those with better predictive powerThis is not a regression evaluation metricF1 scoreR2AccuracyAdjusted R2