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2024 July07: IIT M AN EXAM QDD2 - 07 Jul 2024

MLP Quiz 124 questions50 marks9 SA · 8 MCQ · 7 MSQafternoon / QDD2

Elapsed 00:00

Comprehension passage - the questions below it refer to this

Consider following common data and answer the given subquestions: question figure

Consider following common data and answer the given subquestions:

Q1SA1 mark

What will be the output of the following code snippet? question figure

What will be the output of the following code snippet? Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q2SA1 mark

What will be the output of the following code snippet? question figure

What will be the output of the following code snippet? Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q3SA2 marks

What will be the output of the following code snippet? question figure

What will be the output of the following code snippet? Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q4SA2 marks

What will be the output of the following code snippet? question figure

What will be the output of the following code snippet? Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q5MSQ2 marksselect all that apply

Given the data, which of the following options will provide the same output?

Q6MSQ2 marksselect all that apply

If you would like to see the most frequent car manufacturers in the dataset, which of the following can be used?

Q7MSQ2 marksselect all that apply

Which of the following choice(s) are correct ?

Null values cannot be interpreted by the model hence we need to handle themaccordingly.
Null values cannot be replaced because we can not manipulate the dataset.
We should let the sklearn or software automatically decide how to handledifferent kinds of missing values.
Different types of representation of missing values could be seen in thedataset.

Q8MSQ2 marksselect all that apply

Which of the following options are true about Pearson correlation matrix ?

Q9MSQ3 marksselect all that apply

Q10MCQ2 marks

We need to preprocess the data before using it for model building due to which of the following reasons ?

Due to errors in data capture, data may contain outliers or missing values.
Different features may be at different scales.
Data contains non numerical features.
All of these

Comprehension passage - the questions below it refer to this

Consider following data and answer the given subquestions: question figure

Consider following data and answer the given subquestions: Each row represents a data point. There are exactly 4 points. The index of points starts from 0 and ends at 3 (included).

Q11MCQ3 marks

Which of the following pairs have the highest euclidean distance? Note: take care of the missing values and adjust accordingly. The options refer to the indices of points.

1 and 2
0 and 2
1 and 3
2 and 3
0 and 1
There is a tie between two or more options.
None of these.

Q12MCQ2 marks

Which of the following pairs have the smallest euclidean distance? Note: take care of the missing values and adjust accordingly. The options refer to the indices of points.

1 and 2
0 and 2
1 and 3
2 and 3
0 and 1
There is a tie between two or more options.
None of these.

Comprehension passage - the questions below it refer to this

Consider following data and code snippet: question figure

Consider following data and code snippet: Based on the above data, answer the given subquestions.

Q13SA2 marks

What will be the output of the following code snippet: question figure

What will be the output of the following code snippet: Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q14SA2 marks

What will be the output of the following code snippet: question figure

What will be the output of the following code snippet: Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q15SA2 marks

What will be the output of the following code snippet: question figure

What will be the output of the following code snippet: Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q16SA2 marks

What will be the output of the following code snippet: question figure

What will be the output of the following code snippet: Enter -1, if you think the above code snippet will generate an error.

Your numeric answer:

Q17MSQ2 marksselect all that apply

Which of the following can be used to get the simpleimputer object?

Comprehension passage - the questions below it refer to this

Go through the code snippet given below and answer the given subquestions. question figure

Go through the code snippet given below and answer the given subquestions.

Q18MCQ2 marks

Which of the following options will be the output of the given code?

Q19MCQ2 marks

-0.528
1
0.528
Given code will return an error

Q20MCQ3 marks

Consider the following code: question figure

Consider the following code: Which of the following may be the correct output of the above code?:

Q21MCQ3 marks

Consider the following code: question figure

Consider the following code: Which of the following is more likely to be true?

Q22SA2 marks

What will be the output of the following code ? question figure

What will be the output of the following code ?

Your numeric answer:

Q23MCQ2 marks

It controls the learning rate of the stochastic regressor during training.
It determines the maximum number of iterations for the training process.
It defines the fraction of the validation set used for early stopping.
It specifies the tolerance level for early stopping based on the change in thevalidation error.

Q24MSQ2 marksselect all that apply

Consider the following code block and if needed make appropriate assumptions: question figure

Consider the following code block and if needed make appropriate assumptions: Which of the following may be appropriate to be filled in the blank space value for scoring parameter?

Answer key

Q1 (SA, 1 marks): 11
Q2 (SA, 1 marks): 3
Q3 (SA, 2 marks): 2
Q4 (SA, 2 marks): 2
Q5 (MSQ, 2 marks): A, B, C, D, E
Q6 (MSQ, 2 marks): A, B
Q7 (MSQ, 2 marks): A, D
Q8 (MSQ, 2 marks): A, C
Q9 (MSQ, 3 marks): B, D
Q10 (MCQ, 2 marks): D
Q11 (MCQ, 3 marks): C
Q12 (MCQ, 2 marks): B
Q13 (SA, 2 marks): 4
Q14 (SA, 2 marks): 0.5
Q15 (SA, 2 marks): 1.0
Q16 (SA, 2 marks): 0.4
Q17 (MSQ, 2 marks): B, C
Q18 (MCQ, 2 marks): A
Q19 (MCQ, 2 marks): D
Q20 (MCQ, 3 marks): B
Q21 (MCQ, 3 marks): B
Q22 (SA, 2 marks): 0.76 to 0.80
Q23 (MCQ, 2 marks): D
Q24 (MSQ, 2 marks): B, C

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. Pandas and NumPy

DataFrame Operations: Creating, manipulating, and querying DataFrames.
- df.shape, df.describe(), df.value_counts(), df.query()
- df.iloc[], df.loc[]
Handling Missing Values: Using SimpleImputer from sklearn.impute.
Data Transformation: Using MinMaxScaler, MaxAbsScaler, and OrdinalEncoder from sklearn.preprocessing.

2. Scikit-Learn

Pipelines: Creating and using Pipeline and ColumnTransformer for data preprocessing.
- Pipeline([('impute', SimpleImputer()), ('scale', MinMaxScaler())])
- ColumnTransformer([('impute_scale', impute_scale_pipe, [0, 1]), ('categorical', OrdinalEncoder(), [2])])
Model Training and Evaluation: Using SGDRegressor, LinearRegression, and cross_val_score.
- SGDRegressor(max_iter=1000, tol=1e-3, eta0=0.04, power_t=5, n_iter_no_change=3, validation_fraction=0.3, random_state=42)
- LinearRegression(fit_intercept=False)
- cross_val_score(estimator=LinearRegression(), X=X_train, y=y_train, cv=shuffle_split, scoring='neg_mean_squared_error')
Model Evaluation Metrics: Using r2_score from sklearn.metrics.
- r2_score(y_test, y_pred)

3. Data Splitting

ShuffleSplit: Using ShuffleSplit for splitting data into training and test sets.
- ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)

4. Mathematical Concepts

Euclidean Distance: Calculating the distance between data points.
- $\text{Euclidean Distance} = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2}$
Correlation Matrix: Understanding Pearson correlation coefficients and their range.
- $\text{Pearson Correlation Coefficient} = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$
R² Score: Evaluating the performance of a regression model.
- $R^2 = 1 - \frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y})^2}$

5. Handling Missing Data

Imputation: Techniques for handling missing data, such as mean, median, or mode imputation.
Data Cleaning: Importance of preprocessing data to handle errors, outliers, and missing values.

6. Model Parameters

Tolerance (tol): Understanding the role of tolerance in optimization algorithms.
Learning Rate (eta0): Understanding the learning rate in stochastic gradient descent.
Early Stopping: Using n_iter_no_change and validation_fraction for early stopping in model training.