The weights parameter can be set to 'uniform' or to 'distance'. Setting weights='uniform' means that the k nearest neighbors of a point have equal influence on how that point gets classified. On the other hand, weights='distance' causes closer neighbors to have greater influence.

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Useful Parameters for KNN Classifier

https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

Documentation for sklearn.neighbors.KNeighborsClassifier

The power parameter p controls which distance metric is used in determining neighbors of a point. The default is p=2, which specifies Euclidean distance. Setting p=1 specifies Manhattan distance (aka city block distance), which calculates distance along a grid-like path. Manhattan distance is preferred for high-dimensional data.

"p" Parameter for KNN Classifier

"weights" Parameter for KNN Classifier

The n_neighbors parameter specifies the number of neighbors to consider when classifying points. This number is sometimes referred to as "K". A high n_neighbors risks overfitting the test data and being poor at predicting new data (high bias). On the other hand, a low n_neighbors risks underfitting the data, hence oversimplifying the model and resulting in a "jumpy" plot of predictions (high variance). The best value for K will depend on the dataset, so one value for K may work well for some datasets, but not for others.

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