The Concept of KNN Algorithm Using R

Understanding the Concept of KNN Algorithm Using R

 

The huge amount of data that we’re generating every day, has led to an increase of the need for advanced Machine Learning Algorithms. One such well-performed algorithm is the K Nearest Neighbour algorithm.

In this blog on KNN Algorithm In R, we will understand what is KNN algorithm in Machine Learning and its unique features including the pros and cons, how the KNN algorithm works, an essay example of it, and finally moving to its implementation of KNN using the R Language.

It is quite essential to know Machine Learning basics. Here’s a brief introductory section on what is Machine Learning and its types.

Machine learning is a subset of Artificial Intelligence that provides machines the power to find out automatically and improve from their gained experience without being explicitly programmed.

There are mainly three types of Machine Learning discussed briefly below:

1. Supervised Learning: It is that part of Machine Learning in which the data provided for teaching or training the machine is well labeled and so it becomes easy to work with it.

2. Unsupervised Learning: It is the training of information using a machine that is unlabelled and allowing the algorithm to act on that information without guidance.

3. Reinforcement Learning: It is that part of Machine Learning where an agent is put in an environment and he learns to behave by performing certain actions and observing the various possible outcomes which it gets from those actions.

Now, moving to our main blog topic,

What is KNN Algorithm?

KNN which stands for K Nearest Neighbor is a Supervised Machine Learning algorithm that classifies a new data point into the target class, counting on the features of its neighboring data points. 

Let’s attempt to understand the KNN algorithm with an essay example. Let’s say we want a machine to distinguish between the sentiment of tweets posted by various users. To do this we must input a dataset of users’ sentiment(comments).  And now, we have to train our model to detect the sentiments based on certain features. For example, features such as labeled tweet sentiment i.e., as positive or negative tweets accordingly. If a tweet is positive, it is labeled as 1 and if negative, then labeled as 0.

Features of KNN algorithm: 

• KNN is a supervised learning algorithm, based on feature similarity.

• Unlike most algorithms, KNN is a non-parametric model which means it does not make any assumptions about the data set. This makes the algorithm simpler and effective since it can handle realistic data.

• KNN is considered to be a lazy algorithm, i.e., it suggests that it memorizes the training data set rather than learning a discriminative function from the training data.

• KNN is often used for solving both classification and regression problems.