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📌 Subject: Machine Learning
📌 Week: 6
📌 Session: NPTEL 2025
📌 Course Link: NPTEL Machine Learning
📌 Reliability: Expert-reviewed answers
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NPTEL Introduction to Machine Learning Week 6 Assignment Answers 2025
1. Statement: Decision Tree is an unsupervised learning algorithm.
Reason: The splitting criterion use only the features of the data to calculate their respective measures
- Statement is True. Reason is True.
- Statement is True. Reason is False
- Statement is False. Reason is True
- Statement is False. Reason is False
Answer :- For Answers Click Here
2. Increasing the pruning strength in a decision tree by reducing the maximum depth:
- Will always result in improved validation accuracy.
- Will lead to more overfitting
- Might lead to underfitting if set too aggressively
- Will have no impact on the tree’s performance.
- Will eliminate the need for validation data.
Answer :-
3. What is a common indicator of overfitting in a decision tree?
- The training accuracy is high while the validation accuracy is low.
- The tree is shallow.
- The tree has only a few leaf nodes.
- The tree’s depth matches the number of attributes in the dataset.
- The tree’s predictions are consistently biased.
Answer :-
4. Consider the following statements:
Statement 1: Decision Trees are linear non-parametric models.
Statement 2: A decision tree may be used to explain the complex function learned by a neural network.
- Both the statements are True.
- Statement 1 is True, but Statement 2 is False.
- Statement 1 is False, but Statement 2 is True.
- Both the statements are False.
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5. Entropy for a 50-50 split between two classes is:
- 0
- 0.5
- 1
- None of the above
Answer :-
6. Consider a dataset with only one attribute(categorical). Suppose, there are 10 unordered values in this attribute, how many possible combinations are needed to find the best split-point for building the decision tree classifier?
- 1024
- 511
- 1023
- 512
Answer :-
7. Consider the following dataset:
What is the initial entropy of Malignant?
- 0.543
- 0.9798
- 0.8732
- 1
Answer :-
8. For the same dataset, what is the info gain of Vaccination?
- 0.4763
- 0.2102
- 0.1134
- 0.9355
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Introduction to Machine Learning Week 6 NPTEL Assignment Answers 2024
1. Entropy for a 90-10 split between two classes is:
- 0.469
- 0.195
- 0.204
- None of the above
Answer :- a
2. Consider a dataset with only one attribute(categorical). Suppose, there are 8 unordered values in this attribute, how many possible combinations are needed to find the best split-point for building the decision tree classifier?
- 511
- 1023
- 512
- 127
Answer :- d
3. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are three training data points with the following outputs: 5, 7, 9.6, and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. The average value of the outputs of data points denotes the response of a branch. The original responses for data points along the two branches (left & right respectively) were response−left and, response−right and the new response after collapsing the node is response−new. What are the values for response−left, response−right and response−new (numbers in the option are given in the same order)?
- 9.6, 11, 10.4
- 7.2; 10; 8.8
- 5, 10.5, 15
- depends on the tree height.
Answer :- b
4. Which of the following is a good strategy for reducing the variance in a decision tree?
- If improvement of taking any split is very small, don’t make a split. (Early Stopping)
- Stop splitting a leaf when the number of points is less than a set threshold K.
- Stop splitting all leaves in the decision tree when any one leaf has less than a set threshold K points.
- None of the Above.
Answer :- b
5. Which of the following statements about multiway splits in decision trees with categorical features is correct?
- They always result in deeper trees compared to binary splits
- They always provide better interpretability than binary splits
- They can lead to overfitting when dealing with high-cardinality categorical features
- They are computationally less expensive than binary splits for all categorical features
Answer :- c
6. Which of the following statements about imputation in data preprocessing is most accurate?
- Mean imputation is always the best method for handling missing numerical data
- Imputation should always be performed after splitting the data into training and test sets
- Missing data is best handled by simply removing all rows with any missing values
- Multiple imputation typically produces less biased estimates than single imputation methods
Answer :- d
7. Consider the following dataset:
Which among the following split-points for feature2 would give the best split according to the misclassification error?
- 186.5
- 188.6
- 189.2
- 198.1
Answer :- c
Conclusion:
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