The score method returns a float between 0 and 1 indicating the percentage of test labels predicted correctly. This can be understood as calling something like predict() on X_test and then comparing the output labels to y_test. A high percentage indicates greater accuracy. This method can be used after the model has been fitted

score(X_test, y_test) for sklearn.neighbors.KNeighborsClassifier

After training the classifier, we can use the predict method, which takes in new data instances and returns the predicted corresponding labels (classes). It does so using averages of properties as calculated when fitting the estimator.

predict(X_test) for sklearn.neighbors.KNeighborsClassifier

After splitting the data and creating the knn classifier object, we can use the fit method, which trains ("fits") the model using the training data. This is necessary so that the classifier can predict the labels of test data. Note that X_train is a dataframe of items and their features, whereas y_train is a series of corresponding true labels.

fit(X_train, y_train) for sklearn.neighbors.KNeighborsClassifier

- fit(X_train, y_train)
- predict(X_test)
- score(X_test, y_test)

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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