Infosys Certified Expert - Machine Learning Professional — Free Questions

Question 1

Which of the following algorithm doesn’t uses learning Rate as of one of its hyperparameter?
1. Gradient Boosting
2. Extra Trees
3. AdaBoost
4. Random Forest

Accepted Answer

Only 1 & 2.

Question 2

What is correct statement about an ensembled classifier?
A. Classifiers that are more “sure” can vote with more conviction
B. Most of the times, it performs better than a single classifier
C. Classifiers can be more “sure” about a particular part of the space

Accepted Answer

Only A & B.

Question 3

Which of the are main benefits of ensemble models?
A. Better performance
B. Generalized models
C. Better interpretability

Accepted Answer

Only A & B.

Question 4

Which of the correct statement about weak learners used in ensemble model?
A. They have low variance and they don’t usually overfit
B. They have high bias, so they can not solve hard learning problems
C. They have high variance and they don’t usually overfit

Accepted Answer

Only A & B.

Question 5

Suppose we have ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively.
Which of the following method(s) can be used to combine the predictions of these models?
1. Median
2. Product
3. Average
4. Weighted sum
5. Minimum and Maximum
6. Generalized mean rule

Accepted Answer

1, 3 and 4.

Question 6

Suppose, we have a binary classification problem, we have developed 3 models each with 70% accuracy. 
If we want to ensemble these models using majority voting method. 
What will be the maximum accuracy we can get?

Accepted Answer

1.

Question 7

What are the advantages of stacking?
A. More robust model
B. Better prediction
C. Lower time of execution

Accepted Answer

Only A & B.

Question 8

Which of the following method can be done as one of the steps in stacking?
1. Divide the training data into k folds
2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold
3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms

Accepted Answer

Only 1 & 2.

Question 9

Which of the following is true about bagging?
1. Bagging can be parallel
2 The aim of bagging is to reduce bias not variance
3. Bagging helps in reducing overfitting

Accepted Answer

Only 1 & 2.

Question 10

Suppose, we have 3000 different models with their predictions and we want to ensemble predictions of best x models.
Please suggest, what is/are a possible method to select the best x models for an ensemble?

Accepted Answer

Step wise forward selection.

Infosys Certified Expert - Machine Learning Professional

10 Infosys Certified Expert - Machine Learning Professional practice questions with answers

Which of the following algorithm doesn’t uses learning Rate as of one of its hyperparameter? 1. Gradient Boosting 2. Extra Trees 3. AdaBoost 4. Random Forest

What is correct statement about an ensembled classifier? A. Classifiers that are more “sure” can vote with more conviction B. Most of the times, it performs better than a single classifier C. Classifiers can be more “sure” about a particular part of the space

Which of the are main benefits of ensemble models? A. Better performance B. Generalized models C. Better interpretability

Which of the correct statement about weak learners used in ensemble model? A. They have low variance and they don’t usually overfit B. They have high bias, so they can not solve hard learning problems C. They have high variance and they don’t usually overfit

Suppose we have ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. Generalized mean rule

Suppose, we have a binary classification problem, we have developed 3 models each with 70% accuracy. If we want to ensemble these models using majority voting method. What will be the maximum accuracy we can get?

What are the advantages of stacking? A. More robust model B. Better prediction C. Lower time of execution

Which of the following is true about bagging? 1. Bagging can be parallel 2 The aim of bagging is to reduce bias not variance 3. Bagging helps in reducing overfitting

Suppose, we have 3000 different models with their predictions and we want to ensemble predictions of best x models. Please suggest, what is/are a possible method to select the best x models for an ensemble?

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