KNN Review

1. Data with n features can be conceptualized as points lying in n-dimensional space.

2. Data points can be compared using a distance formula.

3. A point with an unknown class can be classified using K-Nearest Neighbors.

4. To verify the effectiveness of a classifier, data with known classes can be split into a training set and a validation set.

5. Classifiers have parameters that can be tuned to increase effectiveness.

6. Python’s sklearn can be used for many classification and machine learning algorithms.