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IIT M DIPLOMA AN4 EXAM QDD4 25 Feb 2024 - 25 Feb 2024
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Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions.
Choose the correct options to determine the count of NULL values in each column of a pandas DataFrame named ’df’
Which option will help in selecting data of odd indexed columns only? (ie. age, parch are odd indexed 1 and 3 respectively)
What is datatype of sex column in the given titanic dataset
- number
- bool
- str
- object
Which option will help in filtering data to find females who are alive after titanic incident?
What is the given code below trying to accomplish for the given titanic dataset ?
What is the given code below trying to accomplish for the given titanic dataset ?
What is the given code below trying to accomplish for the given titanic dataset ? 
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.
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.
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
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
Which of the following metrics indicate higher their value, better the regression model’s performance?
- RMSE
- R2
- Mean absolute error
- Mean squared error
- 10
- 12
- 14
- 16
- 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.
- 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.
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
- 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
Which of the following are valid loss functions for SGDClassifier?
- Squared Loss
- Hinge Loss
- Log Loss
- Mean Absolute Error
- Column indexed at 0
- Column indexed at 1
- Column indexed at 2
- Column indexed at 3
- Column indexed at 4
- No columns
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
Which of the following feature(s) will be selected in X_selected from X ?
- malic_acid
- magnesium
- flavanoids
- proanthocyanins
- color_intensity
- proline
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 and Data Handling
DataFrame Operations: Handling DataFrames, checking for NULL values, selecting columns, and filtering data.
df.isnull().sum(): Counts NULL values in each column.df.iloc[:, 1:2]: Selects odd-indexed columns.df[(df['sex'] == 'female') & (df['alive'] == 'yes')]: Filters data based on conditions.
Data Types: Understanding data types in DataFrames.
df['sex'].dtype: Determines the data type of a column.
2. Data Visualization
Visualization Techniques: Using visualizations to understand data structure and relationships.
- Heatmaps, scatter plots, box plots, and histograms.
- Histograms: Reveal scale, skew, and modes in the distribution of numerical features.
3. Data Preprocessing
StandardScaler: Standardizes features by removing the mean and scaling to unit variance.
StandardScaler().fit_transform(data): Standardizes the data.
Label Encoding and One-Hot Encoding: Converting categorical data into numerical format.
LabelEncoder().fit_transform(data): Encodes categorical data.OneHotEncoder().fit_transform(data): One-hot encodes categorical data.
Handling Missing Values: Using imputers to handle missing data.
SimpleImputer(strategy="median"): Imputes missing values with the median.
4. Machine Learning Concepts
Train-Test Split: Splitting data into training and testing sets.
train_test_split(data, test_size=0.3, random_state=42): Splits data into training and testing sets.
Model Evaluation Metrics: Metrics for evaluating regression models.
- R² (R-squared): Higher values indicate better performance.
- RMSE (Root Mean Squared Error): Lower values indicate better performance.
- MAE (Mean Absolute Error): Lower values indicate better performance.
Grid Search and Hyperparameter Tuning: Using
GridSearchCVto find the best hyperparameters.GridSearchCV(estimator, param_grid, scoring='neg_mean_squared_error'): Performs grid search for hyperparameter tuning.
5. Feature Selection
Variance Threshold: Selecting features based on variance.
VarianceThreshold(threshold=0).fit_transform(data): Selects features with variance above a threshold.
SelectKBest: Selecting the best features based on statistical tests.
SelectKBest(chi2, k=3).fit_transform(X, y): Selects the top k features.
6. Mathematical Equations
- R² (R-squared):
- RMSE (Root Mean Squared Error):
- MAE (Mean Absolute Error):