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IIT M DIPLOMA AN2 EXAM QDD2 25 Feb 2024 - 25 Feb 2024

MLP Quiz 124 questions50 marks16 MCQ · 7 MSQ · 1 SADiploma / QDD2

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Comprehension passage - the questions below it refer to this

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

Q1MCQ2 marks

Choose the correct options to determine the count of NULL values in each column of a pandas DataFrame named ’df’

Q2MCQ2 marks

Which option will help in selecting data of odd indexed columns only? (ie. age, parch are odd indexed 1 and 3 respectively)

Q3MCQ2 marks

What is datatype of sex column in the given titanic dataset

number
bool
str
object

Q4MCQ2 marks

Which option will help in filtering data to find females who are alive after titanic incident?

Q5MCQ2 marks

What is the given code below trying to accomplish for the given titanic dataset ? question figure

What is the given code below trying to accomplish for the given titanic dataset ?

Q6MCQ1 mark

Q7MCQ2 marks

You are working on a machine learning project that aims to predict housing prices based on various features of the houses. As the first step, you decide to perform exploratory data analysis and visualize the data to understand its structure and relationships. Which of the following visualization techniques or principles is LEAST likely to provide meaningful insights for this kind of regression problem?

Plotting a heatmap of the correlation matrix to understand the linearrelationship between the numeric features.
Using a scatter plot to visualize the relationship between the square footage ofa house and its price.
Visualizing the distribution of housing prices using a pie chart.
Creating box plots for housing prices, grouped by the number of bedrooms, todetect outliers and understand the distribution across different categories.

Q8MCQ2 marks

A company collects 40000 samples (examples) to build a Machine Learning model for an application. They decide to use 30% of the total samples for testing (to be stored in the variable testset) and the rest 70% for training (to be stored in the variable trainset). They also want to sample the same set of samples across multiple runs. Which of the following line (statement) achieves this task? Assume that all samples are stored in the variable data.

Q9MCQ2 marks

Choose the option based on the following statements: Statement 1: The train-test split allows us to simulate the model’s performance on new data by reserving a portion of the dataset for testing. Statement 2: Separating the dataset into training and testing sets helps prevent data leakage, where information from the test set unintentionally influences the model during training. This ensures a fair assessment of the model’s generalization capabilities.

Statement 1 is True and statement 2 is False
Statement 1 is False and statement 2 is True
Both the statements are False
Both the statements are True

Q10MCQ2 marks

In which of the following scenarios is data cleaning most required?

Data has missing values and categorical string data.
Data consists a lot of outliers based on values in target(y) column
The data consists solely of numerical values and are on a similar scale.
None of the choices

Q11MCQ2 marks

Q12MCQ2 marks

Which of the following metrics indicate higher their value, better the regression model’s performance?

RMSE
R2
Mean absolute error
Mean squared error

Q13MCQ2 marks

Q14MCQ2 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.

Q15MCQ2 marks

Accuracy, Accuracy
R2 score, R2 score
Accuracy, R2 score
R2 score, Accuracy
F1 score, Precision
Precision, Recall
MAE, MSE
The code will result in an error.

Q16MCQ3 marks

Q17MSQ2 marksselect all that apply

Suppose that we plot the histogram of numerical features in a data set. This reveals which of the following information?

Scale of the features
(left or right) Skew of the distribution
Modes in the distribution
Missing values

Q18MSQ2 marksselect all that apply

house.data.shape will give the count of number of rows and columns forfeatures and labels(target) both
house.target_names will give the name of the target column
house.data.head() will show last 5 rows of the data
house.feature_names will give a list of names of the columns of the featurematrix

Q19MSQ2 marksselect all that apply

Which of the following are valid loss functions for SGDClassifier?

Squared Loss
Hinge Loss
Log Loss
Mean Absolute Error

Q20MSQ2 marksselect all that apply

Column indexed at 0
Column indexed at 1
Column indexed at 2
Column indexed at 3
Column indexed at 4
No columns

Q21MSQ2 marksselect all that apply

Which of the following Evaluation metrics can be used in a regression problem?

Mean Squared Error
Accuracy
F1-Score
Mean Absolute Error
credit score
CGPA

Q22MSQ3 marksselect all that apply

Which of the following feature(s) will be selected in X_selected from X ? question figure

malic_acid
magnesium
flavanoids
proanthocyanins
color_intensity
proline

Q23MSQ3 marksselect all that apply

Q24SA2 marks

Your numeric answer:

Answer key

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

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 DataFrame Operations

Handling NULL Values: Use df.isnull().sum() to count NULL values in each column.
Selecting Odd Indexed Columns: Use df.iloc[::, 1::2] to select odd indexed columns.
Data Types: Understanding data types in pandas, such as object for strings.
Filtering Data: Use df[(df['sex'] == 'female') & (df['alive'] == 'yes')] to filter data based on conditions.

2. Data Preprocessing

Handling Missing Values: Use SimpleImputer to fill missing values with mean, median, or other strategies.
Standard Scaling: Use StandardScaler to scale data to have a mean of 0 and a standard deviation of 1.
One-Hot Encoding: Use OneHotEncoder to convert categorical data into numerical format.
Label Encoding: Use LabelEncoder to convert categorical data into numerical labels.

3. Machine Learning Models and Evaluation

Linear Regression: Use LinearRegression for regression problems.
Logistic Regression: Use LogisticRegression for classification problems.
Evaluation Metrics: Use metrics like RMSE, R2 score, Mean Absolute Error for regression problems.
Grid Search: Use GridSearchCV for hyperparameter tuning.

4. Data Visualization

Heatmaps: Use heatmaps to understand the linear relationship between numeric features.
Scatter Plots: Use scatter plots to visualize the relationship between two variables.
Box Plots: Use box plots to detect outliers and understand the distribution across different categories.
Histograms: Use histograms to reveal the scale, skew, and modes of the distribution of numerical features.

5. Feature Selection

Variance Threshold: Use VarianceThreshold to remove features with low variance.
SelectKBest: Use SelectKBest with statistical tests like chi2 to select the best features.

6. Model Training and Evaluation

Train-Test Split: Use train_test_split to split data into training and testing sets.
Early Stopping: Use early_stopping in models like SGDRegressor to stop training if the validation score does not improve.
Loss Functions: Understand different loss functions like Hinge Loss and Log Loss for classification problems.

Equations

1. Standard Scaling

z = \frac{x - \mu}{\sigma}

where $z$ is the scaled value, $x$ is the original value, $\mu$ is the mean, and $\sigma$ is the standard deviation.

2. Mean Squared Error (MSE)

\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2

where $y_i$ is the actual value and $\hat{y}_i$ is the predicted value.

3. R2 Score

R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}

where $\bar{y}$ is the mean of the actual values.

4. Mean Absolute Error (MAE)

\text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|