ML basics

What is a "model" in machine learning?

• A model is a smaller representation of the thing you're studying.

• A model is a mathematical relationship derived from data that an ML system uses to make predictions

• A model is a piece of computer hardware

If you wanted to use an ML model to predict energy usage for commercial buildings, what type of model would you use?

• Classification

• Regression

What distinguishes a supervised approach from an unsupervised approach?

• A supervised approach is given data that contains the correct answer.

• A supervised approach typically uses clustering.

• An unsupervised approach knows how to label clusters of data.

What attributes of a dataset would be ideal to use for ML?

• Small size / Low diversity

• Small size / High diversity

• Large size / Low diversity

• Large size / High diversity

Why does a model need to be trained before it can make predictions?

• A model doesn't need to be trained. Models are available on most computers.

• A model needs to be trained to learn the mathematical relationship between the features and the label in a dataset.

• A model needs to be trained so it won't require data to make a prediction.