3. (a) (b) (C) Explain briefly how the error rate is defined in classification. Is there an ideal classifier? If yes, describe it. How can we compute it in realworld problems? Assume we would like to predict the COVlD-19 average daily vaccination rate based on TV advertisements and we are building a model (budget spent on TV ads as predictor and vaccination rate as response). When assessing the overall model accuracy, we often use the R-squared (R2) statistic. Assume for our model we receive the R2 value of 0.7. What does this mean? When looking for the best fit for a sample data, having different options (models) with different degrees, does it make sense taking the model with the highest R2 value? Justify your