3 Classification Fundamentals Free Online FlashCards

What is classification?

Classification is supervised machine learning that learns from labeled examples to assign inputs to categories, or classes.

What is a true positive?

A true positive (TP) occurs when the classifier predicts positive and the actual class is positive.

What is binary classification?

Binary classification is a classification task with exactly two possible classes.

What does a labeled example pair?

A labeled example pairs an input with its known answer, called the label.

What are features in classification?

Features are measurable properties used to describe an input, such as an email's word count or whether it contains a link.

What is a test set used for?

A test set is labeled data kept separate from training to estimate how well a classifier predicts new examples.

How is classification accuracy calculated?

Accuracy is the fraction of all predictions that are correct: $\frac{TP + TN}{TP + TN + FP + FN}$.

What is multiclass classification?

Multiclass classification is a classification task with more than two possible classes.

How should a model use a labeled example's label?

The label is the target the model learns to predict, not a feature to supply as input.

What is a false positive?

A false positive (FP) occurs when the classifier predicts positive but the actual class is negative.

What is a false negative?

A false negative (FN) occurs when the classifier predicts negative but the actual class is positive.

What is a true negative?

A true negative (TN) occurs when the classifier predicts negative and the actual class is negative.