True or False? Statement: Euclidean distance is used as one of the predictive performance measures for a nearest-neighbor model.

Consider this evaluation function for Chess: 9* (QWQB) + 5*(RW - RB) + 3*(KW - KB) + 3*(BW - BB) + 1*(PW - PB) where: QW denotes the number of Queens that White has, QB denotes the number of Queens that Black has, and so on (the shapes and letters for pieces are shown below). ด KING QUEEN BISHOP KNIGHT ROOK PAWN Q B K R P Calculate the evaluation function value for the following game state: 1 ♡ 7

A satisficing model will discover a decent, but not necessarily the best, solution to a problem. True/False

: |: Section 3 - Random Forests Decision Trees on their own are effectiev classifiers. The true power, though, comes from a forest of trees -- multiple decision trees working together as an ensemble learner. We create multiple trees, each using a subset of the available attributes, let them each make a guess at the correct classification, and then take the majority vote as the predicted class. Sounds tricky, right? Once again, sklearn to the rescue. We are going to see if a random forest can improve on our decision tree accuracy for the bank data. To begin, we are going to create a Random Forest using the wheat training and test data from the previous section. First, reload our data. 1 # reload the wheat dataset from UCI 2 3 df = pd. read_csv ("seeds_dataset.txt", sep='\\t', engine='python') 4 6 7 df.columns = ['a', 'p', 'compactness', 'length', 'width', 'coeff', 'length_g', 'type'] print (f'Our data has {df.shape [0]} rows and {df.shape [1]} columns') 8 #Mark 70% of the data for…