Machine Learning Pipeline and Models Quiz

Which of the following is NOT typically part of the machine learning pipeline?

• Data collection

• Model training

• Hardware optimization

• Feature engineering

What is the primary difference between supervised and unsupervised learning?

• Supervised learning requires more data

• Supervised learning uses labeled data

• Unsupervised learning is more accurate

• Unsupervised learning requires more computational power

In which step of the ML pipeline would you handle missing values?

• Model selection

• Model evaluation

• Data preprocessing

• Model deployment

What is the purpose of train-test splitting?

• To make models train faster

• To evaluate model performance on unseen data

• To reduce computational requirements

• To eliminate the need for cross-validation

A good practice for data splitting in ML projects is:

• 90% training, 10% testing

• 50% training, 50% testing

• 70-80% training, 20-30% testing

• 30% training, 70% testing

What is tokenization primarily used for?

• Converting categorical variables to numerical

• Breaking text into smaller units like words or characters

• Normalizing continuous variables

• Removing outliers from the dataset

Linear regression is used for:

• Classification problems

• Regression problems

• Clustering problems

• Dimensionality reduction

The cost function commonly used in linear regression is:

• Cross-entropy loss

• Hinge loss

• Mean squared error

• Kullback-Leibler divergence

Which assumption is NOT required for linear regression?

• Linear relationship between features and target

• Independence of observations

• Target variable must be categorical

• Homoscedasticity (constant variance of errors)

Logistic regression is primarily used for:

• Regression problems

• Binary classification

• Multi-class classification without modification

• Time series forecasting