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

Information gain can be evaluated by splitting based on  which of the following in Decision Tree?

In general, which of the following method(s) is used for predicting continuous dependent variables? Linear Regression Logistic Regression

Which of the following is TRUE about K-means clustering? A. K-means is very sensitive to the initializations of the cluster centers B. Bad initialization of the cluster centers can lead to poor convergence speed C. Bad initialization can lead to poor overall clustering results

Dataframe columns be indexed by/as:

Common technique(s) for dimensionality reduction include(s) which of the following:

One way to get a diverse set of classifiers is to use the same training algorithm for every predictor, but train them on different random subsets of the training set. Two forms of this are known as bagging and pasting. Bagging is done when sampling with the above method is performed _____________, while Pasting is done when sampling with the above method is performed _____________.

Can we pass data with null values to machine learning model building?

Decision Trees are often prone to high variance, and the model is not likely to generalize well because they tend to: