def recommend(features,feature_list): neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean') neighbors.fit(feature_list) distances, indices = neighbors.kneighbors([features]) return indices

What parameter in KNN determines the distance metric used to find the nearest neighbors?Answer arean_neighborsmetricweightsalgorithm

Which library in Python is commonly used for implementing K-Nearest Neighbors (KNN)?Answer areaNumPySciPyscikit-learnTensorFlow

Does the following code snippet correctly generate the top-N recommendations for a user using collaborative filtering? def collab_generate_top_N_recommendations(user, N=10, k=20):    if user not in user_sim_df.columns:        return book_ratings.groupby('title').mean().sort_values(by='rating', ascending=False).index[:N].to_list()    sim_users = user_sim_df.sort_values(by=user, ascending=False).index[1:k+1]    favorite_user_items = []    most_common_favorites = {}    for i in sim_users:        max_score = util_matrix_norm.loc[:, i].max()        favorite_user_items.append(util_matrix_norm[util_matrix_norm.loc[:, i] == max_score].index.tolist())    for item_collection in range(len(favorite_user_items)):        for item in favorite_user_items[item_collection]:            if item in most_common_favorites:                most_common_favorites[item] += 1            else:                most_common_favorites[item] = 1    sorted_list = sorted(most_common_favorites.items(), key=operator.itemgetter(1), reverse=True)[:N]    top_N = [x[0] for x in sorted_list]    return top_NNo, it does not consider the rating values.Yes, it correctly generates top-N recommendations.No, it lacks the calculation of user similarity.No, it does not handle the 'Cold-start problem' properly.

#<GRADED> def findknn(xTr,xTe,k): """ function [indices,dists]=findknn(xTr,xTe,k); Finds the k nearest neighbors of xTe in xTr. Input: xTr = nxd input matrix with n row-vectors of dimensionality d xTe = mxd input matrix with m row-vectors of dimensionality d k = number of nearest neighbors to be found Output: indices = kxm matrix, where indices(i,j) is the i^th nearest neighbor of xTe(j,:) dists = Euclidean distances to the respective nearest neighbors """ # Enter your code here n,d = xTr.shape m,d = xTe.shape tempDists = l2distance(xTr,xTe); indices=np.argsort(tempDists,axis=0)[:k]; dists=tempDists[indices,np.arange(tempDists.shape[1])[None, :]]; raise NotImplementedError('Your code goes here!') return indices, dists # until here #</GRADED> using python and without using for loop

#<GRADED> def findknn(xTr,xTe,k): """ function [indices,dists]=findknn(xTr,xTe,k); Finds the k nearest neighbors of xTe in xTr. Input: xTr = nxd input matrix with n row-vectors of dimensionality d xTe = mxd input matrix with m row-vectors of dimensionality d k = number of nearest neighbors to be found Output: indices = kxm matrix, where indices(i,j) is the i^th nearest neighbor of xTe(j,:) dists = Euclidean distances to the respective nearest neighbors """ # Enter your code here raise NotImplementedError('Your code goes here!') return indices, dists # until here #</GRADED> without using the for loop in python