K-Nearest Neighbors

Estimates conditional probability of a Bayes classifier
Given a positive integer $K$ and a test observation $x_0$, the KNN classifier first identifies the $K$ points in the training data that are closest to $x_0$, represented by $N_0$. It then estimates the conditional probability for class $j$ as the fraction of points in $N_0$ whose response values equal $j$:

KNN classifies the test observation $x_0$ to the class with the highest probability.
The value of $K$ determines the flexibility of a KNN model

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