Data and Analytics Machine Learning Fundamentals

Question 1

Which type of machine learning involves training a model on labeled data to predict outcomes for new, unseen data?

Accepted Answer

Supervised learning

Answer

Supervised learning uses labeled training data where the correct output is known, allowing the model to learn mappings from inputs to outputs.

Question 2

What is overfitting in a machine learning model?

Accepted Answer

The model learns training data too well and fails to generalize to new data

Answer

Overfitting occurs when a model memorizes training data noise and details, resulting in high training accuracy but poor performance on unseen data.

Question 3

Which technique is used to prevent overfitting by adding a penalty term to the loss function?

Accepted Answer

Regularization

Answer

Regularization adds a penalty (L1 or L2) to the loss function to discourage the model from fitting noise and reduce overfitting.

Question 4

In machine learning, what does the term 'feature engineering' refer to?

Accepted Answer

Transforming raw data into meaningful input variables for a model

Answer

Feature engineering is the process of using domain knowledge to create, transform, or select variables (features) from raw data to improve model performance.

Question 5

What is the purpose of a validation set in machine learning?

Accepted Answer

To tune hyperparameters and select the best model during development

Answer

A validation set is used during model development to tune hyperparameters and compare different models, separate from the test set used for final evaluation.

Data and Analytics Practice Test

Data and Analytics Practice Test

Data and Analytics Machine Learning Fundamentals