Given below are three scatter plots for two features (Images 1, 2 & 3, from left to right).

In the above images, which of the following is/are examples of multi-collinear features?

Given below is a scenario for training error TE and Validation error VE for a machine learning algorithm M1.

You want to choose a hyperparameter (H) based on TE and VE.

Instructions: Each question may have any number of correct choices. Clearly mark (tick)

all choices you believe to be correct (1 mark for each correct choice, −0.5 for each incorrect

choice).

Which of the following might be valid reasons for preferring an SVM over a neural network?

Imagine you are solving a classification problem with a highly imbalanced class.

The majority class is observed 99% of the time in the training data. Your model has 99% accuracy after taking the predictions on the test set. Which of the following is true in such a case?

1. The accuracy metric is not a good idea for imbalanced class problems.
2. The accuracy metric is a good idea for imbalanced class problems.
3. Precision and recall metrics are good for imbalanced class problems.
4. Precision and recall metrics aren’t good for imbalanced class problems.

What would you do in PCA to get the same projection as SVD?

Suppose you are given 7 Scatter plots 1-7 (left to right), and you want to compare Pearson correlation coefficients between variables of each scatterplot.

Which of the following is in the right order?

Which of the following options is/are true for K-fold cross-validation?

1. An increase in K will result in a higher time required to cross-validate the result.
2. Higher values of K will result in higher confidence in the cross-validation result as compared to a lower value of K.
3. If K=N, then it is called Leave one out cross validation, where N is the number of observations.

What are machine learning types?

Does Supervised Learning consider when no teacher is available?

Point the right order of CRISP-DM?