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

Unsupervised learning

Answer

Reinforcement learning

Answer

Self-supervised learning

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

The model performs poorly on both training and test data

Answer

The model is too simple to capture patterns in the data

Answer

The model requires too much computational power

Question 3

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

Accepted Answer

Regularization

Answer

Boosting

Answer

Bagging

Answer

Normalization

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

Selecting the best algorithm for a task

Answer

Evaluating model performance on a test set

Answer

Deploying a trained model to production

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

To train the model's parameters

Answer

To provide a final unbiased evaluation of the model

Answer

To augment the training data

Question 6

Which of the following is an example of an unsupervised learning algorithm?

Accepted Answer

K-means clustering

Answer

Linear regression

Answer

Random forest

Answer

Support vector machine

Question 7

What does the bias-variance tradeoff describe in machine learning?

Accepted Answer

The balance between underfitting (high bias) and overfitting (high variance)

Answer

The tradeoff between model accuracy and training speed

Answer

The tradeoff between the number of features and sample size

Answer

The balance between precision and recall