ISA 2 ML test 1

Choose the correct answer - suppose you have a dataset which can be trained with 100% accuracy of a Decision tree with depth 8.

• Depth 5 will be high variance and low bias

• Depth 2 will be low variance and low bias

• Depth 6 will be low variance and high bias

• Depth 10 will be high variance and high bias

You’ve just finished training a random forest for spam classification, and it is getting abnormally bad performance on your validation set, but good performance on your training set. Your implementation has no bugs. What could be causing the problem?

• Your decision trees are too deep

• You are randomly sampling too many features when you choose a split

• You have too few trees in your ensemble

• all of them

What will happen when you add a new feature in linear regression model?

• increase the r-square value

• decrease the r-square value

• no-effect

• depends on the feature

What is one reason not to use the same data for both your training set and your testing set?

• data will overfit the model.

• data will choose wrong algorithm.

• not have enough data for both.

• data will underfit the model.

What is an example of a commercial application for a machine learning system?

• data entry system

• product recommendation system

• massive data repository

• data warehouse system

What type of Machine Learning Algorithm is suitable for predicting the dependent variable with two different values?

• Logistic Regression

• Linear Regression

• Multiple Linear Regression

• Polynomial Regression

The entropy of a given dataset is zero. This statement implies what?

• further splitting is required

• no further splitting is required

• Need some other information to decide splitting

• None of the Mentioned

Consider a Confusion Matrix of a classifier where True Positive is 61, False Positive is 8, True Negative is 38 and False Negative is 5 then what is the correct statement

• Accuracy is 81%

• Misclassification Rate is 19%

• Type-I Error is 8

• Type-II Error is 13

Suppose there is a basket and it is filled with some fresh fruits. The task is to arrange the same type of fruits at one place. but there is no information about those fruits beforehand, it’s the first time that the fruits are being seen or discovered. What kind of machine learning technique it is?

• Reinforcement Learning

• Supervised Learning

• Unsupervised Learning

• Semi-Supervised Learning

Which of the following is a disadvantage of decision trees?

• Factor analysis

• Decision trees are robust to outliers

• Decision trees are prone to be overfit

• Decision Tree requires less effort for data preparation