Neighbour Classifier Based on the Euclidean Distance - IT Assignment Help

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Assignment 3 [Part 1/2]
1.
a) Consider a dataset D that contains only two observations x1=(1,1) and x2=(−1,−1). Suppose that the class of the first observation is y1=0 and that the class of the second observation is y2=1. How would a 1-nearest neighbour classifier based on the Euclidean distance classify the observation x=(2,3)? What are the distances between this new observation and each observation in the dataset? [0.5/5]
b) Consider a dataset D that only contains observations of two different classes. Explain why a k-nearest neighbour classifier does not need a tie-breaking policy when k is odd. [0.5/5]
c) Explain why a classifier that obtains an accuracy of 99.9% can be terrible for some datasets. [0.5/5]
d) Consider a classifier tasked with predicting whether an observation belongs to class y (positive class). Suppose that this classifier has precision 1.0 and recall 0.1 on a test dataset. If this classifier predicts that an observation does not belong to class y, should it be trusted? Should it be trusted if it predicts that the observation belongs to class y? [0.5/5]

2.
a) What is the pair of classes that is most confusing for the 1- nearest neighbour classifier trained in the previous sections? [0.5/5]

b) Train a support vector machine classifier using the same training dataset used in the previous sections and compute its accuracy on the corresponding test dataset. You can use the default hyperparameters for the class SVC from sklearn.svm. Show the code in the report. [0.75/5]

3. Using the same training dataset used in the previous sections, employ GridSearchCV to find the best hyperparameter settings based on5-fold cross-validation for a RandomForestClassifier.Consider n_estimators ∈{50,100,200} and max_features ∈{0.1,0.25}. Use the default values for the remaining hyperparameters. Compute the accuracy of the best model on the corresponding test dataset. Show the code in the report. [0.75/5]

4. The function kmeans_update presented below is part of an implementation of the k-means clustering algorithm. The variable X is a
matrix (numpy array) where each row corresponds to an observation.
Explain in detail each line of this function. You can refer to each (non- empty) line by a number between 1 and 6. [1/5] def kmeans_update(X, cluster_centers):y_pred = np.argmin(cdist(X, cluster_centers), axis=1) next_cluster_centers = np.zeros(cluster_centers.shape)  for i in range(len(next_cluster_centers)):next_cluster_centers[i] = X[y_pred == i].mean(axis=0)  return y_pred, next_cluster_centers

Assignment 3 [Part 2/2]
1.
a) What is the advantage of using the Apriori algorithm in comparison with computing the support of every subset of an itemset in order to find the frequent itemsets in a transaction dataset? [0.5/5]
b) Let L1 denote the set of frequent 1-itemsets. For k≥2, why must every frequent k-itemset be a superset of an itemset in L1? [0.5/5]
c) Let L2={{1,2},{1,5},{2,3},{3,4},{3,5}}. Compute the set of candidates C3 that is obtained by joining every pair of joinable itemsets from L2. [0.5/5]

2.
a) Let S1 denote the support of the association rule {popcorn, soda}⇒{movie}. Let S2 denote the support of the association rule {popcorn}⇒{movie}. What is the relationship between S1 and S2? [0.5/5]
b) What is the support of the rule {}⇒{Kidney Beans} in the transaction dataset used in the tutorial presented above? [0.5/5]

3. a) In the transaction dataset used in the tutorial presented above, what is the maximum length of a frequent itemset for a support threshold of 0.2? [0.5/5]
b) Implement a function that receives a DataFrame of frequent itemsets and a strong association rule (represented by a frozenset of antecedents and a frozenset of consequents). This function should return the corresponding Kulczynski measure. Include the code in your report. [1/5]

4. Implement a function that receives a DataFrame of frequent itemsets and a strong association rule (represented by a frozenset of antecedents and a frozenset of consequents). This function should return the corresponding imbalance ratio. Include the code in your report. [1/5]

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