Bayes Theorem and KNN algorithm

1. Bayes Theorem and KNN Algorithm

2. Bayes Theorem

3. KNN algorithm
 The K-Nearest Neighbors (KNN) algorithm is a supervised machine
learning algorithm for classification and regression
 It can be used to solve classification and regression problems.
 The output depends on whether k-nearest neighbors are used for
classification or regression. The main idea behind K-NN is to find the K
nearest data points, or neighbors, to a given data point and then
predict the label or value of the given data point based on the labels or
values of its K nearest neighbors.
 K can be any positive integer, but in practice, K is often small

4. Distance Metrics

5. Predict the answer

6. Key aspects of K-nearest neighbour's
 In the k-nearest neighbor’s classification, the output is a class
membership. An object is classified by a majority vote of its
neighbors, with the object being assigned to the class most
common among its k nearest neighbors (k is a positive integer,
typically small). If k = 1, then the object is simply assigned to
the class of that single nearest neighbor.
 In the K-nearest neighbors regression, the output is the property
value for the object. This value is the average of the values of its
k nearest neighbors.

7.  K-nearest neighbor is a non-parametric method, which means
that it does not make any assumptions about the underlying
data.
 This is advantageous over parametric methods, which do make
such assumptions. The models don’t learn parameters from the
training data set to come up with a discriminative function in
order to classify the test or unseen data set

8. Predict the output for K=3 and k=5

9. What is the most appropriate value
of K