1) Select the intention of the following machine learning algorithms:

from sklearn.neighbors import KNeighborsClassifier

classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

classifier.fit(X_train, y_train) kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)

y_kmeans = kmeans.fit_predict(X)

Group of answer choices

a. overfit the data for classification and underfit the data for the clustering

b. Look for 5 closest neighbors and form 5 clusters

c. Only form 5 clusters

d. underfit the data for classification and overfit the data for the clustering

2) The cost function that is used in logistic regression is:

Group of answer choices

a. Mean Squared Error

b. Both of these

c. None of these

d. Maximum Likelihood