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

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.

"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.

"n_neighbors" Parameter for KNN Classifier

University of Michigan - Ann Arbor

Scikit-learn enables the creation of classifier objects. For example, 
```python
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, weights = 'distance')
```
In this example, the classifier object (named knn) will consider 5 neighbors in its kneighbors queries.   Setting the weights parameter to 'distance' means that closer neighbors will be more influential than those farther away  (in terms of detemrining which category the "target" point belongs to).

sklearn.neighbors.KNeighborsClassifier

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

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