Question 1

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

Accepted Answer

To clean and format raw data to make it suitable for analysis

Answer

To increase the size of the dataset

Answer

To generate random numbers

Answer

To visualize the data in 3D

Question 2

Which of the following techniques is used for handling missing data during data preprocessing?

Accepted Answer

Imputation

Answer

Normalization

Answer

Feature selection

Answer

Encoding

Question 3

What does feature scaling achieve in data preprocessing?

Accepted Answer

It standardizes the range of independent variables

Answer

It categorizes data into classes

Answer

It reduces the number of features

Answer

It merges multiple datasets

Question 4

Which technique is commonly used to convert categorical variables into numerical values during data preprocessing?

Accepted Answer

One-hot encoding

Answer

Logistic regression

Answer

K-means clustering

Answer

PCA

Question 5

What is the purpose of removing outliers during data preprocessing?

Accepted Answer

To improve the accuracy of the model

Answer

To create more features

Answer

To increase the dataset size

Answer

To simplify the data structure

Question 6

Which of the following methods is used to handle imbalanced datasets during data preprocessing?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique)

Answer

Principal component analysis

Answer

Support vector machines

Answer

Linear regression

Question 7

What does the term 'data normalization' refer to in the context of data preprocessing?

Accepted Answer

Scaling data to a specific range, usually [0, 1]

Answer

Aggregating data to summarize information

Answer

Dividing data into training and testing sets

Answer

Removing duplicate entries from the dataset

Question 8

Which of the following is a technique used for dimensionality reduction during data preprocessing?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Data splitting

Answer

Label encoding

Answer

Data encoding

Question 9

What is the purpose of feature selection in data preprocessing?

Accepted Answer

To identify and retain the most relevant features for the model

Answer

To increase the number of features available

Answer

To convert features into categorical variables

Answer

To ensure all features have the same scale

Question 10

Which method can be used to identify the importance of features in a dataset?

Accepted Answer

Feature importance scores from tree-based models

Answer

P-value from statistical tests

Answer

Cluster analysis results

Answer

Mean absolute error calculations

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