Glossary¶

Use this page to record terms and ideas that help you understand professional analytics projects.

This project covers classification: building and evaluating models that predict a category from input features.

Pro-tip: Expand the VS Code Outline view (below the navigator on the right) to see this file organization at-a-glance.

Training and Evaluation¶

training set¶

The training set is the portion of data used to fit the model. The model sees these examples and adjusts its internal parameters to minimize error.

test set¶

The test set is the portion of data held back from training and used only to evaluate the model on unseen examples. A good test score suggests the model generalizes beyond the training data.

train-test split¶

A train-test split divides the dataset into a training portion and a test portion. A common split is 80% training and 20% test.

overfitting¶

Overfitting happens when a model learns the training data too closely and performs poorly on new, unseen data. A large gap between training accuracy and test accuracy is a sign of overfitting.

accuracy¶

Accuracy is the fraction of predictions that are correct. It is a useful metric when classes are roughly balanced but can be misleading when one class is much more common than others.

confusion matrix¶

A confusion matrix shows how often a classifier predicted each class compared to the true class. Each row represents the true class; each column represents the predicted class. Off-diagonal entries are misclassifications.

precision¶

Precision is the fraction of positive predictions that were actually correct. High precision means the model rarely predicts a class when it should not.

recall¶

Recall is the fraction of actual positives that the model correctly identified. High recall means the model rarely misses a true positive.

F1 score¶

The F1 score is the harmonic mean of precision and recall. It balances both concerns and is useful when false positives and false negatives both carry real cost.

classification report¶

A classification report shows precision, recall, and F1 score for each class in the target. It gives a fuller picture of model performance than accuracy alone.

Classification Models¶

decision tree¶

A decision tree is a classifier that makes predictions by asking a sequence of yes/no questions about feature values. It is easy to interpret but can overfit if grown too deep.

logistic regression¶

Logistic regression is a linear classifier that estimates the probability that an input belongs to each class. Despite the name, it is a classification model, not a regression model.

k-nearest neighbors (k-NN)¶

k-NN is a classifier that predicts the class of a new point by finding the k most similar training examples and taking a majority vote. It is simple but can be slow on large datasets.