We classify the same day: Overcast Hot High False Likelihoods: P("Yes

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Why do we need a K-NN algorithm

We classify the same day:
Overcast
Hot
High
False
Likelihoods:
P("Yes
") = 5/12 x 3/12 x 4/11 x 7/11 x 10/16 ≈ 0.015
P("No
") = 1/8 x 3/8 x 5/7 x 3/7 x 6/16 ≈ 0.005
(Normalized) probabilities:
P("Yes
") = 0.015/(0.015+0.05)≈75%Play="Yes"
P("No
") = 0.05/(0.015+0.05)≈25%
K-Nearest Neighbor (KNN) Algorithm
K-Nearest Neighbour is one of the simplest Machine Learning al-
gorithms based on Supervised Learning technique. K-NN algorithm as-
sumes the similarity between the new case/data and available cases and
put the new case into the category that is most similar to the available
categories. K-NN algorithm stores all the available data and classifies a
new data point based on the similarity. This means when new data ap-
pears then it can be easily classified into a well suite category by using
K-NN algorithm. K-NN algorithm can be used for Regression as well as
for Classification but mostly it is used for the Classification problems.
K-NN is a non-parametric algorithm, which means it does not make
any assumption on underlying data and It is also called a lazy learner
algorithm because it does not learn from the training set immediately
instead it stores the dataset and at the time of classification, it performs
an action on the dataset. KNN algorithm at the training phase just stores
the dataset and when it gets new data, then it classifies that data into a
category that is much similar to the new data.
Why do we need a K-NN algorithm? Suppose there are two cate-
gories, i.e., Category A and Category B, and we have a new data point
x1, so this data point will lie in which of these categories. To solve this
type of problem, we need a K-NN algorithm. With the help of K-NN, we

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can easily identify the category or class of a particular dataset. Consider
the below diagram:

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