K-nearest neighbors (KNN)

-

K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems. However, it is mainly used for classification predictive problems in industry. 

The following two properties would define KNN well 

Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.

Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.

Working of KNN Algorithm

K-nearest neighbours (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set. We can understand its working with the help of the following steps −

Step 1 − For implementing any algorithm, we need a dataset. So during the first step of KNN, we must load the training as well as test data.

Step 2 − Next, we need to choose the value of K i.e. the nearest data points. K can be any integer.

Step 3 − For each point in the test data do the following −

  • 3.1 − Calculate the distance between test data and each row of training data with the help of any of the method namely: Euclidean, Manhattan or Hamming distance. The most commonly used method to calculate distance is Euclidean.

  • 3.2 − Now, based on the distance value, sort them in ascending order.

  • 3.3 − Next, it will choose the top K rows from the sorted array.

  • 3.4 − Now, it will assign a class to the test point based on the most frequent class of these rows.

Step 4 − End

Comments

Post a Comment

Popular posts from this blog

Linear Regression with single variable

-
Image
Linear Regression Algorithm From Andrew Ng’s Machine learning course The above diagram conveniently gives a brief overview of the Linear Regression Algorithm. We feed our learning algorithm with some data set(training set), which then outputs a function called Hypothesis. Hypothesis approximates a target function for mapping inputs to outputs. For linear regression in one variable, our hypothesis function is of the form — h(x) = θ0 + θ1x where θ0 and θ1 are the parameters.
Read more

Introduction Deep Learning

-
Deep learning is a branch of  machine learning   which is completely based on   artificial neural networks , as the neural network is going to mimic the human brain so deep learning is also a kind of mimic of the human brain. In deep learning, we don’t need to explicitly program everything. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture. A formal definition of deep learning is- neurons "Deep learning is a particular kind of machine learning that achieves great  power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones". In the human brain app...
Read more

Artificial Neural Network (ANN)

-
Image
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural network is usually a computational network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons that are linked to each other in various layers of the networks. These neurons are known as nodes. Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building blocks, unsupervised learning, Genetic algorithm, etc. What is Artificial Neural Network? The term " Artificial Neural Network " is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one...
Read more