Virtual Labs

Data Preprocessing and Feature Engineering

What is the main purpose of data preprocessing in machine learning?

a: To increase dataset size Explanation

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b: To improve data quality and consistency Explanation

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c: To reduce computation speed Explanation

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d: To remove all features Explanation

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Which of the following problems is commonly addressed during data preprocessing?

a: Overfitting Explanation

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b: Model deployment Explanation

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c: Missing values Explanation

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d: Hyperparameter tuning Explanation

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Handling missing values in a dataset helps to:

a: Increase noise in data Explanation

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b: Prevent errors during model training Explanation

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c: Reduce number of records Explanation

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d: Change target labels Explanation

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Normalization is mainly applied to:

a: Categorical attributes Explanation

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b: Target variables Explanation

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c: Numerical attributes Explanation

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d: Class labels Explanation

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Why is normalization important in machine learning?

a: It converts text into numbers Explanation

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b: It removes duplicate values Explanation

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c: It brings features to a common scale Explanation

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d: It increases dataset size Explanation

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Encoding is a technique used to:

a: Reduce dimensionality Explanation

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b: Handle missing data Explanation

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c: Convert categorical data into numerical form Explanation

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d: Remove outliers Explanation

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Which of the following is an example of a categorical variable?

a: Age Explanation

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b: Salary Explanation

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c: Gender Explanation

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d: Height Explanation

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Feature engineering mainly focuses on:

a: Collecting raw data Explanation

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b: Creating and transforming features Explanation

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c: Removing datasets Explanation

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d: Deploying models Explanation

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Data visualization is used to:

a: Train machine learning models Explanation

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b: Encode categorical data Explanation

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c: Understand patterns and relationships in data Explanation

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d: Reduce memory usage Explanation

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Which step is performed first in a typical machine learning pipeline?

a: Model evaluation Explanation

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b: Model training Explanation

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c: Data preprocessing Explanation

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d: Prediction Explanation

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