Determine whether the following formulas are true. So note that N does not contain 0 in this case. 1. Vz e N : 3y e Z : |¤ * y| < 10 2. 3z €N : Vy e Z : |z * y| > 10 3. Va eN : 3b e R :6² = a 4. V a e N : Vbe N : Vc e Z : a² + b² = c² O 1. is true O 2. is true O 3. is true O 4. is true

7. Let the statements A, B be given by A: not(p or q), B: (not p) or q. Let T = true, F = false. If p = T and q = F, then O A = T, B = T O A = F, B = T O A = F, B = F O A= T, B = F

python help Q1: Over or Under Define a procedure over-or-under which takes in a number x and a number y and returns the following: -1 if x is less than y 0 if x is equal to y 1 if x is greater than y *Hint: You can use cond (define (over-or-under x y)  'YOUR-CODE-HERE   )   ;;; Tests (over-or-under 1 2) ; expect -1 (over-or-under 2 1) ; expect 1 (over-or-under 1 1) ; expect 0     Q2: Filter Write a procedure filter-lst, which takes a predicate f and a list lst, and returns a new list containing only elements of the list that satisfy the predicate. The output should contain the elements in the same order that they appeared in the original list. (define (filter-lst f lst)    'YOUR-CODE-HERE   )   ;;; Tests (define (even? x)   (= (modulo x 2) 0)) (filter-lst even? '(0 1 1 2 3 5 8)) ; expect (0 2 8)

1) Select the score given by the following code from sklearn import metrics ‘Precision Score': metrics.precision_score(y_test, y_pred) Group of answer choices a. Positive Predictive Value b. Recall c. Accuracy d. Negative predictive value   2) from sklearn import metrics 'Recall Score':    metrics.recall_score(y_test, y_pred) Group of answer choices a.True Positive Rate b. Accuracy c. Positive Predictive Value d. Negative predictive value   3) What the method below does?X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.4) Group of answer choices a. produces the training data b. Splits the data into train and test subsets c. produces the target output d. produces the test data