Hello learners In this article we are going to discuss NPTEL Introduction To Machine Learning – IITKGP Assignment 1 Answers. All the Answers provided below to help the students as a reference, You must submit your assignment with your own knowledge and use this article as reference only.
About the course:-
This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms.
| Assignment No. | Answers |
|---|---|
| Deep Learning IIT Ropar Assignment 1 | Click Here |
| Deep Learning IIT Ropar Assignment 2 | Click Here |
| Deep Learning IIT Ropar Assignment 3 | Click Here |
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| Deep Learning IIT Ropar Assignment 12 | Click Here |
NPTEL Introduction To Machine Learning – IITKGP Assignment 1 Answers 2023:
1. Which of the following is/are classification tasks?
a. Find the gender of a person by analyzing his writing style
b. Predict the price of a house based on floor area. number of rooms. etc.
C. Predict whether there will be abnormally heavy rainfall next year
d. Predict the number of conies of a book that will be sold this month
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2. A feature F1 can take certain values: A, B, C, D, E, F, and represents the grade of students from a college. Which of the following statement is true in the following case?
a. Feature F1 is an example of a nominal variable.
b. Feature F1 is an example of an ordinal variable.
c. It doesn’t belong to any of the above categories.
d. Both of these
Answer :-
3. Suppose I have 10,000 emails in my mailbox out of which 200 are spams. The spam detection system detects 150 emails as spams, out of which 50 are actually spam. What is the precision and recall of my spam detection system?
a. Precision = 33.333%. Recall = 25%
b. Precision = 25%, Recall = 33.33%
c. Precision = 33.33%, Recall = 75%
d. Precision = 75%, Recall = 33.33%
Answer :-
Next Week Answers: Assignment 02
4. Which of the following statements describes what is most likely TRUE when the amount of training data increases?
a. Training error usually decreases and generalization error usually increases.
b. Training error usually decreases and generalization error usually decreases.
C. Training error usually increases and generalization error usually decreases.
d. Training error usually increases and generalization error usually increases.
Answer :- 5. You trained a leaming algorithm, and plot the learning curve. The following figure is obtained.
The algorithm is suffering from
a. High bias
b. High variance
c. Neither
Answer :-
6. I am the marketing consultant of a leading e-commerce website. I have been given a task of making a system that recommends products to users based on their activity on Facebook. I realize that user interests could be highly variable. Hence, I decide to
T1) Cluster the users into communities of like-minded people and
T2) Train separate models for each community to predict which product category (e.g., electronic gadgets. cosmetics. etc.) would be the most relevant to that community.
The task T1 is a/an _________ learning problem and T2 is a/an ________problem.
Choose from the options:
a. Supervised and unsupervised
b. Unsupervised and supervised
c. Supervised and supervised
d. Unsupervised and unsupervised learning problem and I2 is a/an
Answer :-
7. Select the correct equations.
TP – True Positive, IN – True Negative, FP – False Positive, FN – False Negative
i. Precision = Tp/Tp+Fp
ii Recall = FP/Ty+Fp
ili. Recall = Tp/To+Fn
iv. Accuracy=: Tp+Fn/Tp+Fp+Tn+Fn
a. i, iii. IV
b. i and iii
c. 11 and
iv d. i. ii, iii. iv
Answer :-
8. Which of the following tasks is NOT a suitable machine learning task(s)?
a. Finding the shortest path between a pair of nodes in a graph
b. Predicting if a stock price will rise or fall
c. Predicting the price of petroleum
d. Grouping mails as spams or non-spams
Answer :-
9. Which of the following is/are associated with overfitting in machine learning?
a. High bias
b. Low bias
c. Low variance
d. High variance
e. Good performance on training data
f. Poor performance on test data
Answer :-
10. Which of the following statements about cross-validation in machine learning is/are true?
a. Cross-validation is used to evaluate a model’s performance on the training data.
b. Cross-validation guarantees that a model will generalize well to unseen data.
c. Cross-validation is only applicable to classification problems and not regression problems.
d. Cross-validation helps in estimating the model’s performance on unseen data by simulating the test phase.
Answer :-
11. What does k-fold cross-validation involve in machine learning?
a. Splitting the dataset into k equal-sized training and test sets.
b. Splitting the dataset into k unequal-sized training and test sets.
c. Partitioning the dataset into k subsets, and iteratively using each subset as a validation set while the remaining k-1 subsets are used for training.
d. Dividing the dataset into k subsets, where each subset represents a unique class label for classification tasks.
Answer :-
12. What does the term “feature space” refer to in machine learning?
a. The space where the machine learning model is trained.
b. The space where the machine learning model is deployed.
c. The space which is formed by the input variables used in a machine leaming model.
d. The space where the output predictions are made by a machine learning model.
Answer :-
13. Which of the following statements is/are true regarding supervised and unsupervised learning?
a. Supervised learning can handle both labeled and unlabeled data.
b. Unsupervised learning requires human experts to label the data.
c. Supervised learning can be used for regression and classification tasks.
d. Unsupervised learning aims to find hidden patterns in the data.
Answer :-
14. One of the ways to mitigate overfitting is
a. By increasing the model complexity
b. By reducing the amount of training data
c. By adding more features to the model
d. By decreasing the model complexity
Answer :-
15. How many Boolean functions are possible with N features?
a. (22N)
b. (2N)
C. (N2)
d. (4N)
Answer :-
