NPTEL Introduction to Machine Learning Assignment Week 6 Answers

Are you looking for NPTEL Introduction to Machine Learning Assignment Week 6 Answers? If yes, you will find the answers to the questions asked in the NPTEL Introduction to Machine Learning quiz exam here. If you are preparing for this exam this article will help you in finding the latest and updated answers.

There is a total of 8 questions related to Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees – Instability Evaluation Measures. The correct answers are marked in Green Color with a tick sign.

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NPTEL Introduction to Machine Learning Assignment Week 6 Answers

1. What is specified at any non-leaf node in a decision tree?

1. Class of instance
2. Data value description
3. Test specification
4. Data process description

2. Suppose we use the decision tree model for solving a multi-class classification problem. As we continue building the tree, w.r.t. the generalisation error of the model.

1. the error due to bias increases
2. the error due to bias decreases
3. the error due to variance increases
4. the error due to variance decreases

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 3 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 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)?

1. 21.6, 40, 61.6
2. 7.2: 10;8.8
3. 3, 4, 7
4. depends on the tree height.

4. Which of these classifiers do not require any additional modifications to their original descriptions (as seen in the lectures) to use them when we have more than 2 classes? (multiple options may be correct)

1. decision trees
2. logistic regression
3. support vector machines
4. k nearest neighbors

5. Consider the following data set.

Considering profitable’ as the binary valued attribute we are trying to predict, which of the attributes would you select as the root in a decision tree with multi-way splits using the cross-entropy impurity measure

1. price
2. maintenance
3. capacity
4. airbag

6. In the above data set, what is the value of cross entropy when we consider capacity as the attribute to split on (multi-way splits)? (You can round-off the cross entropy value to the nearest 4-decimal place number)

1. 0.7973
2. 0.8684
3. 0.8382
4. 0.7688

7. An important factor that influences the variance of decision trees is the average height of the tree. For the same dataset, if we limited the height of the trees to some H, how would the variance of the decision tree algorithm be affected?

1. Variance may increase with tree length H.
2. Variance may decrease with tree length H.
3. Variance is unaffected by tree length H.

8. In which of the following situations is it appropriate to introduce a new category ‘Missing’ for missing values? (multiple options may be correct)

1. When values are missing because the 108 emergency operator is sometimes attending a very urgent distress call.
2. When values are missing because the attendant spilled coffee on the papers from which the data was extracted.
3. When values are missing because the warehouse storing the paper records went up in flames and burnt parts of it.
4. When values are missing because the nurse/doctor finds the patient’s situation too urgent.

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