Feature scaling an important step to do before applying the…
Feature scaling an important step to do before applying the K-means Clustering algorithm. Choose the best reason(s) for why it is useful. A. It will give the same weightage for all features in distance calculation B. It will result in the same number of clusters when it is used C. It is needed when calculating Cosine distance, but not needed for Euclidean distance
Read DetailsImplementing standard Linear Regression is done as the follo…
Implementing standard Linear Regression is done as the following: from sklearn.linear_model import LogisticRegression lr = LogisticRegression().fit(X_train, y_train) How do you implement the Logistic Regression model using L1 and L2 regularization? A. from sklearn.linear_model import LogRegCV lr_l1 = LogRegCV(Cs=10, cv=4, penalty=’l1′, solver=’liblinear’).fit(X_train, y_train) lr_l2 = LogRegCV(Cs=10, cv=4, penalty=’l2′).fit(X_train, y_train) B. from sklearn.linear_model import LogisticRegressionCV lr_l1 = LogisticRegressionCV(Cs=10, cv=4, penalty=’l1′, solver=’liblinear’).fit(y_train) lr_l2 = LogisticRegressionCV(Cs=10, cv=4, penalty=’l2′).fit(X_train, y_train) C. from sklearn.linear_model import LogisticRegressionCV lr_l1 = LogisticRegressionCV(Cs=10, cv=4, penalty=’l1′, solver=’liblinear’).pred(X_train, y_train) lr_l2 = LogisticRegressionCV.fit(X_train, y_test) D. from sklearn.linear_model import LogisticRegressionCV lr_l1 = LogisticRegressionCV(Cs=10, cv=4, penalty=’l1′, solver=’liblinear’).fit(X_train, y_train) lr_l2 = LogisticRegressionCV(Cs=10, cv=4, penalty=’l2′).fit(X_train, y_train)
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