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MLP week 10 - real exam questions
6 questions pulled from past papers whose wording matches this week's topics, newest first, duplicates removed. Pick your answers, then press Check answers. Figures and answer keys are the originals.
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Consider the following assertion and reason. Select the correct option. Assertion (A): Increasing the number of estimators in AdaBoost can sometimes reduce the test accuracy, even though the training error continues to decrease. Reason (R): In AdaBoost, after each iteration, the weights of correctly classified training samples are increased so that future weak learners focus more on these correct samples. Choose the correct option:
- Both A and R are true, and R is the correct explanation of A.
- Both A and R are true, but R is not the correct explanation of A.
- A is true, but R is false.
- A is false, but R is true.
Consider the following adaboost models:
Which of the following statements are MOST accurate?
Which of the following statements are MOST accurate?- adb1 is less likely to overfit than adb2 because its base trees are shallower.
- Increasing n_estimators always improves test accuracy for AdaBoost.
- Learning rate affects the contribution of each base learner; a smaller learning rate always reduces training accuracy.
- adb2 with deeper trees will have lower bias but higher variance than adb1.
Select all the false statements amongst the given statements:
- RandomForest uses sampling without replacement for bagging.
- Boosting reduces bias by sequentially training models
- Bagging reduces bias by training multiple models on different subsets of data and averaging their predictions.
- Boosting trains all base models independently in parallel.
Consider below statements and choose the correct option: Statement1:The bagging technique combines multiple models trained on different subsets of data Statement2:The Boosting technique trains the model Parallelly, focusing on the error made by the other model.
- Statement1 is True, Statement2 is True
- Statement1 is True, Statement2 is False
- Statement1 is False, Statement2 is True
- Statement1 is False, Statement2 is False
Brajesh wants to tune hyper parameters of an AdaBoost model for a classification problem with following specifications: 1. Base estimator as Decision trees classifier with max_depth=1. 2. Number of estimators range from 30 to 40 (both inclusive) at the interval of 1. 3. Cross validation = 5. 4. Learning rate must vary between 0.2 to 2.0 (both inclusive) at the intervals of 0.2. 5. Train the best model on the entire training set. 6. Print score on test set. Which of the following code blocks will correctly execute Brajesh’s task? [Note: Assume necessary imports and variables for training and test sets.]
Rajesh wants to tune hyper parameters of an AdaBoost model for a classification problem with following specifications: 1. Base estimator as Decision trees classifier with max_depth=1. 2. Number of estimators range from 10 to 20 (both inclusive) at the interval of 1. 3. Cross validation =4. 4. Learning rate must vary between 0.1 to 1.0 (both inclusive) at the intervals of 0.1. 5. Train the best model on the entire training set. 6. Print score on test set. Which of the following code blocks will correctly execute Rajesh’s task? [Note: Assume necessary imports and variables for training and test sets.]
