Calculating Classification Probability

Calculating Classification Probability with KNN

Function Signature

def classify_probability(self, query_point, expected_val):

Parameters

• query_point: The point for which classification probability is to be determined.

• expected_val: The label value for which the probability is to be calculated.

Return Value

Returns the probability (in percentage) that the query_point belongs to the class specified by expected_val based on the stored values in the KNN instance.

Description

The classify_probability function calculates the probability that a given query_point belongs to the class specified by expected_val. It first retrieves the nearest neighbors of the query_point using the classify_neighbors function. It then counts how many of these neighbors have the label expected_val and calculates the probability based on this count.

It's important to note that the store_vals function must be called prior to using the classify_probability function to ensure that the necessary values are stored in the KNN instance.

Examples

from deeprai.models import KNN

# Sample data
x_vals = [[1, 2], [2, 3], [3, 4]]
y_vals = [0, 1, 0]
query_point = [2, 2]

# Create an instance of the classifier
classifier = KNN()

# Store the values in the classifier
classifier.store_vals(x_vals, y_vals, p=3, k=2)

# Calculate the probability that the query_point belongs to class 1
probability = classifier.classify_probability(query_point, 1)
print(f"The probability that the query point belongs to class 1 is {probability}%")