Scikit-learn is a very popular Machine Learning library in Python which provides a KNeighborsClassifier object which performs the KNN classification. The n_neighbors parameter passed to the KNeighborsClassifier object sets the desired k value that checks the k closest neighbors for each unclassified point.

The object provides a .fit() method which takes in training data and a .predict() method which returns the classification of a set of data points.

The Euclidean Distance between two points can be computed, knowing the coordinates of those points.

On a 2-D plane, the distance between two points p and q is the square-root of the sum of the squares of the difference between their x and y components. Remember the Pythagorean Theorem: a^2 + b^2 = c^2 ?

We can write a function to compute this distance. Let’s assume that points are represented by tuples of the form (x_coord, y_coord). Also remember that computing the square-root of some value n can be done in a couple of ways: math.sqrt(n), using the math library, or n ** 0.5 (n raised to the power of 1/2).

def distance(p1, p2):
  x_diff_squared = (p1[0] - p2[0]) ** 2
  y_diff_squared = (p1[1] - p2[1]) ** 2
  return (x_diff_squared + y_diff_squared) ** 0.5

distance( (0, 0), (3, 4) )      # => 5.0

Choosing an optimal k value in KNN determines the number of neighbors we look at when we assign a value to any new observation.

For a very low value of k (suppose k=1), the model overfits on the training data, which leads to a high error rate on the validation set. On the other hand, for a high value of k, the model performs poorly on both train and validation set. When k increases, validation error decreases and then starts increasing in a “U” shape. An optimal value of k can be determined from the elbow curve of the validation error.

The K-Nearest Neighbors algorithm is a supervised machine learning algorithm for labeling an unknown data point given existing labeled data.

The nearness of points is typically determined by using distance algorithms such as the Euclidean distance formula based on parameters of the data. The algorithm will classify a point based on the labels of the K nearest neighbor points, where the value of K can be specified.