1 Introduction to Machine Learning Free Online FlashCards

What is machine learning?

Machine learning uses data to learn patterns that a model can use to make predictions or identify structure.

What is a feature in machine learning?

A feature is an input property a model uses, such as a home's floor area or neighborhood.

What is a label in supervised learning?

A label, or target, is the value a supervised-learning model is asked to predict.

What kind of output does regression predict?

Regression predicts a numerical value, such as a home's sale price.

How do supervised and unsupervised learning differ?

Supervised learning uses examples with labels; unsupervised learning looks for structure without supplied labels.

What happens when a model is trained?

During training, the algorithm adjusts the model using training examples so its predictions fit them according to an objective, often called a loss.

What is a validation set used for?

A validation set helps compare models and tune choices during development.

How do MAE and MSE differ?

Mean absolute error measures the average size of prediction errors; mean squared error penalizes larger errors more strongly.

What does it mean for a model to generalize?

A model generalizes when it performs well on new examples, not only on the examples used during training.

What is overfitting, and what pattern can signal it?

Overfitting occurs when a model fits training details or noise too closely and performs poorly on unseen data. A widening gap—training performance improving while validation performance worsens—can signal it.

What kind of output does classification predict?

Classification predicts a category, such as whether an email is spam or not spam.

What does clustering do when labels are unavailable?

Clustering groups similar examples without using known labels; the meaning of the groups may need interpretation.