1. What is an incorrect way to avoid overfitting?
   1. Reduce the number of features manually or do feature selection
   2. Use regularization
   3. Do a cross-validation to estimate the test error
   4. Increase the size of data set

2. What is correct desorption about data set?

1. The validation set is a set of examples that cannot be used for learning the model but can help tune model parameters.
2. The training set is used to assess the performance of the final model and provide an estimation of the test error.
3. Never use the test set in any way to further tune the parameters or revise the model.
4. The test set is a set of examples used for learning a model. ..........................................................................................................................................................................................................................................................3 . Machine Learning Evaluation measures. Consider a dataset with 90 negative examples and 10 positive examples. Suppose a model built using this data predicts 30 of the examples as positive (only 10 of them are actually positive) and 70 as negative. What are the numbers of True Positives (TP), False Positives (FP), True Negatives (TN), False Negatives (FN), Accuracy, Precision, Recall, and Specificity? Show all your steps.