1 Introduction to Machine Learning and Datasets Free Online FlashCards

What is machine learning?

Machine learning builds programs that learn patterns from examples and use them to predict or organize new data.

What does a machine learning model do?

A model captures patterns learned from data and applies them to new examples.

What does regression predict?

Regression predicts a numerical quantity, such as a home's sale price or tomorrow's temperature.

What does classification predict?

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

What is clustering?

Clustering groups examples by similarity when their groups or labels are not provided in advance.

How do supervised and unsupervised tasks differ in their use of labels?

Regression and classification typically use labeled examples; clustering commonly searches for structure without target labels.

In a tabular dataset, what does each row represent?

A dataset is a collection of examples. In a common table format, each row represents one example.

What are features in a dataset?

Features are the input properties used by a model; in a table, they are columns describing examples.

What is a label or target?

A label, or target, is the value a supervised model is meant to predict.

What are two common types of dataset features?

A feature can be numerical, such as home size, or categorical, such as neighborhood.

What data problems can reduce dataset quality?

Missing values, duplicates, incorrect entries, and wrong labels can reduce dataset quality.

What is a training set used for?

A training set contains examples with features and known targets that the model uses to learn patterns.