NPTEL Introduction To Machine Learning Week 6 Assignment Answer 2023

NPTEL Introduction To Machine Learning Week 6 Assignment Solutions

Week 1 NPTEL Introduction To Machine Learning Assignment Answer 2023

NPTEL Introduction To Machine Learning Week 6 Assignment Answer 2023

1. Which of the following is/are major advantages of decision trees over other supervised learning techniques? (Note that more than one choices may be correct)

Theoretical guarantees of performance
Higher performance
Interpretability of classifier
More powerful in its ability to represent complex functions

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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.

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3. 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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4. Consider the following dataset:

What is the initial entropy of Malignant?

0.543
0.9798
0.8732
1

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5. For the same dataset, what is the info gain of Vaccination?

0.4763
0.2102
0.1134
0.9355

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6. Which of the following machine learning models can solve the XOR problem without any transformations on the input space?

Linear Perceptron
Neural Networks
Decision Trees
Logistic Regression

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7. 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.

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8. ______ is a measurement of likelihood of an incorrect classification of a new instance for a random variable, if the new instance is randomly classified as per the distribution of class labels from the data set.

Gini impurity.
Entropy.
Information gain.
None of the above.

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9. 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.

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10. 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? (considering only binary splits)