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IIT M DIPLOMA AN EXAM QDD3 03 Aug - 03 Aug 2025

MLT Quiz 215 questions40 marks7 MCQ · 6 SA · 2 MSQDiploma / afternoon / QDD3

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Q1MCQ4 marks

Q2MCQ3 marks

Q3MCQ3 marks

Setting 3, because it has the lowest training error, indicating the best fit to thetraining data.
Setting 2, because it has the lowest validation error and a relatively lowtraining error, indicating a good generalization.
Setting 1, because it has the highest number of iterations and therefore likelyto be more stable.
Setting 4, because it uses the highest learning rate and converges the fastest.

Q4MCQ3 marks

Q5MCQ2 marks

TRUE
FALSE

Q6MCQ1 mark

TRUE
FALSE

Q7MSQ2 marksselect all that apply

Q8MSQ2 marksselect all that apply

Which among the following are true?

Q9SA3 marks

Your numeric answer:

Comprehension passage - the questions below it refer to this

Based on the above data, answer the given subquestions. question figure

Q10MCQ3 marks

Which among the following represents the correct decision boundary for the decision tree? Note: Here + represents the label 1 and − represents the label 0.

Q11SA2 marks

What will be the prediction for the new datapoint (2, 2)?

Your numeric answer:

Comprehension passage - the questions below it refer to this

Based on the above data, answer the given subquestions. question figure

Q12SA2 marks

Your numeric answer:

Q13SA3 marks

Your numeric answer:

Comprehension passage - the questions below it refer to this

Suppose you want to build a decision tree to help diagnose a certain disease based on patient data. You have a dataset of 1000 patients labeled as either “positive” or “negative”. Here, positive represents patients with the disease and negative represents no disease. One of the feature used to diagnose is headache. Below is the breakdown of the patients reporting headache: Headache present: 300 positive, 50 negative Headache not present: 100 positive, 550 negative question figure

Q14SA3 marks

What is the entropy of the root node? Enter the answer correct to two decimal places.

Your numeric answer:

Q15SA4 marks

What is the information gain for choosing the feature “headache” for splitting at the root node? Enter the answer correct to two decimal places.

Your numeric answer:

Answer key

Q1 (MCQ, 4 marks): A
Q2 (MCQ, 3 marks): C
Q3 (MCQ, 3 marks): B
Q4 (MCQ, 3 marks): C
Q5 (MCQ, 2 marks): A
Q6 (MCQ, 1 marks): A
Q7 (MSQ, 2 marks): A, C
Q8 (MSQ, 2 marks): A, C
Q9 (SA, 3 marks): 4.2
Q10 (MCQ, 3 marks): B
Q11 (SA, 2 marks): 0
Q12 (SA, 2 marks): 0.22 to 0.26
Q13 (SA, 3 marks): 1
Q14 (SA, 3 marks): 0.95 to 0.99
Q15 (SA, 4 marks): 0.34 to 0.38

Concepts this paper tests

Auto-generated summary from the source site. Handy as a checklist; the lessons above explain each idea from scratch.

Summary of Core Topics, Concepts, Principles, and Equations

1. Maximum Likelihood Estimation (MLE)

Concept: MLE is a method of estimating the parameters of a statistical model by maximizing the likelihood function.
Equation:

\hat{w}_{MLE} = \exp \left( \frac{1}{n} \sum_{i=1}^{n} (y_i - \log(x_i)) \right)

2. Maximum A Posteriori (MAP) Estimation

Concept: MAP estimation is used in Bayesian statistics to estimate an unknown quantity by maximizing the posterior distribution.
Equation:

\hat{w}_{\text{MAP}} = \arg \min_{w} \left\{ \| y - X^T w \|^2 + \lambda \| w \|^2 \right\}

\lambda = \frac{\sigma^2}{\gamma^2}

3. Linear Regression

Concept: Linear regression models the relationship between a dependent variable and one or more independent variables using a linear approach.
Equation:

y = w^T x + \epsilon

\min_{w \in \mathbb{R}^d} ||X^T w - y||_2^2

4. Model Evaluation and Hyperparameter Tuning

Concept: Evaluating the performance of a model using training and validation errors to choose the best hyperparameters.
Key Idea: Choose hyperparameters that minimize validation error while keeping training error relatively low.

5. Generative and Discriminative Models

Concept:
- Generative Models: Model how the features are generated, e.g., Gaussian Naive Bayes.
- Discriminative Models: Model the conditional probability $p(y|x)$ , focusing on the boundary between classes.

6. Decision Trees

Concept: A decision tree is a flowchart-like structure where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome.
Entropy and Information Gain:
- Entropy: Measure of impurity or disorder in a dataset.
- Information Gain: Measure of the reduction in entropy achieved by partitioning the data based on a given feature.

7. Naive Bayes Classifier

Concept: A probabilistic classifier based on applying Bayes' theorem with strong independence assumptions between the features.
Equation:

P(Y = c) \prod_{i=1}^{n} P(X_i = x_i | Y = c)