You are working on a classification project to identify whether an individual will default on a bank loan or not. The predictors are the characteristics of the credit of the individual such as the credit score, current loan amount, installment amount, number of times payment was late, etc. The training data set contains 15,000 data samples and 10 predictor variables. You notice that 20 samples are missing random predictor variable values. Upon further inspection, you find the following information: 1) the data set is balanced (i.e., it has a similar proportion of both the classes), 2) the maximum number of predictor variable values that are missing for any of the 20 samples is 2, 3) none of the predictor variables are missing values of more than 2 samples, and 4) 11 out of the 20 samples belong to the same class. What is the best way to handle the missing values?

Coding-based: For the given data set in the python file: Fit a random forest regressor to the same training data set obtained after splitting the data set with the same conditions as in an earlier question (random.seed(123), test_size=0.1 and random_state=1 ) (Set the random_state=0 in the regressor. You may tune the respective parameter: n_estimators to attain the lowest error.) Make predictions on the test data set with the fit obtained on the training data set. Obtain the lowest mean squared error. Select the closest value of the mean squared error that you obtained from the following:

Coding-based: For the given data set in the python file, do the following: Type this in a cell: random.seed(123) And then split the data into train and test data sets with a test_size=0.10 and random_state=1. Which of the following data sets contain record #32 with the following features? 1.35472 0.0 8.14 0 0.5380 6.072 100.0 4.1750 4 307 21.0 376.73 13.04  

The plot below shows data points between 2 predictor variables (x1 and x2) for a 2-class classification problem (red vs blue): What can be said about the relationship between the predictor variables and the response variable? Select one of the following options:

You are applying Adaptive Boosting method on a 2-class classification problem. Below is the table that displays the misclassification rate and the model values that are obtained upon training three models (i.e., m = 3). After training, a sample from the test data set is classified as +1 or -1 by each of these three models (as shown below): Model Misclassification Rate Value Predicted Class (test sample) 1 0.65 -0.310 -1 2 0.27 0.424 1 3 0.5 0 -1 Which class will the sample belong to using the Adaptive Boosting method?

Coding-based: For the given data set in the python file, report the standard deviation of the ‘ZN’ predictor variable.

Which of the following measures will attain the highest score, representing the impurity at a node for a 2-class classification problem, given the probability of an event occurring is 0.5?

The Nineteenth Amendment guaranteed the right to vote to all women, regardless of race or ethnicity.

A work that presents a kind character stricken with a disease stimulates 

When it comes to critical thinking, the Apollonian refers to