Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 6 – IIT Madras? This article will help you with the answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Introduction to Machine Learning Assignment 6 “
What is Introduction to Machine Learning?
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
CRITERIA TO GET A CERTIFICATE
Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100
Final score = Average assignment score + Exam score
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
Below you can find the answers for NPTEL Introduction to Machine Learning Assignment 6
|Introduction to Machine Learning Assignment 1||Click Here|
|Introduction to Machine Learning Assignment 2||Click Here|
|Introduction to Machine Learning Assignment 3||Click Here|
|Introduction to Machine Learning Assignment 4||Click Here|
|Introduction to Machine Learning Assignment 5||Click Here|
|Introduction to Machine Learning Assignment 6||Click Here|
|Introduction to Machine Learning Assignment 7||Click Here|
|Introduction to Machine Learning Assignment 8||Click Here|
|Introduction to Machine Learning Assignment 9||Click Here|
|Introduction to Machine Learning Assignment 10||Click Here|
NPTEL Introduction to Machine Learning Assignment 6 Answers:-
Q1. Which of the following is very interpretable?
Q2. Which of these models are non-parametric?
Q3. Entropy for a 50-50 split between two classes is:
Q4. Statement: Decision Tree is an unsupervised learning algorithm.
Reason: The splitting criterion use only the features of the data to calculate their respective measures
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Q5. 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 maximum 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)?
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Q6. Which among the following split-points for the feature1 would give the best split according to the information gain measure?
Q7. For the same dataset, which among the following split-points for feature2 would give the best split according to the gini index measure?
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Q8. 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?
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