Quiz on Data Basics for Machine Learning

What is a common reason for missing data?

• Data was perfectly recorded.

• All data was collected successfully.

• A survey respondent skipped a question.

• The model was trained without any issues.

What is row-wise deletion?

• Using algorithms to predict missing values.

• Filling missing values with the mean.

• Removing entire columns with missing values.

• Deleting rows that contain missing values.

Which method replaces missing values with the average?

• Mean Imputation

• Forward Fill

• Column-wise Deletion

• Prediction-Based Imputation

What is the best approach when a large part of the dataset is missing?

• Removing the entire dataset.

• Ignoring the missing values.

• Filling with mean/median.

• Using predictive imputation.

Which machine learning models can handle missing data automatically?

• Support Vector Machines

• Linear Regression

• K-Nearest Neighbors

• Decision Trees

What is normalization in machine learning?

• A process to visualize data.

• A way to increase the size of the dataset.

• A technique to bring data values into a similar range.

• A method to delete missing data.

Why is normalization important?

• It makes data collection easier.

• It increases the number of features.

• It prevents features with larger values from dominating.

• It ensures all data is deleted.

What is the range typically used in Min-Max Normalization?

• 1 to 100

• 0 to 1

• 0 to 100

• 0 to 10

What happens if you directly compare scores on different scales?

• Data will be automatically deleted.

• All scores will be normalized.

• The results will be accurate.

• The results will be misleading.

What is the effect of normalization on model learning?

• It only affects the output.

• It slows down the learning process.

• It has no effect on learning.

• It helps models learn better by reducing complexity.