How long should you keep splitting a tree?

Gini Index does include logarithm factor for information gain computation , hence it is difficult to compute.                           

What type of transformation is used for a continuous (numerical value) feature type?  

By utilizing machine learning pipelines, which of the following is made easier?

Performance Metrics: What is the expression used for accuracy in Confusion Metrics?  

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

Visualization can be created in multiple ways by using:

What can go wrong if you tune hyperparameters using the test set?

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)  

A Random Forest is an ____________ of decision trees, that are typically trained with the ___________ method, and usually set the max_samples as the size of the training set