K Nearest Neighbors

K Nearest Neighbors makes predictions for a sample by finding the k nearest samples and averaging their responses.

K Nearest Neighbors makes predictions for a sample by finding the k nearest samples and assigning the most represented class among them.

Warning: this algorithm requires storing the entire training data into the model. This will lead to a very large model if the data is larger than a few hundred lines. Predictions will also be slow.

Distance weighting

Use distance weighting

If enabled, {{isRegression() ? 'averaging' : 'voting'}} across neighbors will be weighed by the inverse distance from the sample to the neighbor.

Neighbor finding algorithm

The method used to find the nearest neighbors to each point. Has no impact on predictive performance, but will have a high impact on training and prediction speed.

Leaf size

Leaf size passed to the Ball or KD Tree. This can affect the speed of the construction and query.

The exponent of the Minkowski metric used to search neighbors. For p = 2, this gives Euclidian distance, for p = 1, Manhattan distance. Greater values lead to the L_p distances.