Write a function rmse that takes in truth and prediction values and returns the root-mean-squared error. Use sklearn’s ‘mean squared error’ class. All variables are as per assignment   A. from sklearn.metrics import mean_squared_error def rmse(ytrue):     return np.sqrt(mean_squared_error(ytrue, ypredicted))   B. from sklearn.metrics import mean_squared_error def rmse(ytrue, ypredicted):     return np.sqrt(mean_squared_error(ypredicted))   C. from sklearn.metrics import mean_squared_error def rmse(ytrue, ypredicted):     return np.sqrt(mean_squared_error(ytrue, ypredicted))   D. from sklearn.metrics import mean_squared_error def rmse(ypredicted):     return np.sqrt(mean_squared_error(ytrue, ypredicted))

Which Pandas one-hot encoder method works well with data that is defined as categorical? All variables are as per assignment A.      data = pd.get_dummies(data, columns=one_hot_encode_cols) B.      data = pd.get_var(data, columns=one_hot_encode_cols) C.       data = pd.get_ohc(data, columns=one_hot_encode_cols) D.       data = pd.get_cols(data, columns=one_hot_encode_cols)

How would you reset the index of a dataframe with a new index given as list? The new index is given by ” new_index” and “df” is a pandas dataframe  

Performance Metrics: If a model has an unclear precision or recall value, what can use to determine if the model is good or not?  

Which of the following is correct for counting the total number of items in a list?

What function is used to pass multiple functions at once in Pandas?

A key difference between Lasso and Ridge regression is that Lasso regression allow you to:  

For the example of Linear Regression, how do you test a dataset applying Linear Regression? Let’s label Linear Regression as “LR”. X_train & Y_train are training dataset and corresponding label  X_test & Y_test are testing dataset and corresponding label  

What does a LabelBinarizer() function do?  

Which plot is useful for visualizing one-dimensional data?