Classification Algorithms in Machine Learning

In classification algorithms, a computer is programmed to specify to which category an entry belongs. Object detection is one of the problems where a classification algorithm can be used. In this article, I will introduce you to all the machine learning classification algorithms explained with Python.

Classification Algorithms in Machine Learning

In simple terms, I will define the classification task as an input category classification task. Some of the issues where classification algorithms can be used are:

  1. Text Categorization
  2. Fraud Detection
  3. Optical Character Recognition
  4. Face Detection
  5. Language Detection
  6. Customer Segmentation

Classification algorithms are used when the task is about to classify this data into a given number of categories and the task of an algorithm is to identify the category of an input variable. Here are the main types of classification algorithms that I will cover in this article:

  1. Decision Tree
  2. Naive Bayes
  3. K Nearest Neighbors
  4. Support Vector Machines
  5. Logistic Regression

Now let’s go through all these classification algorithms in machine learning one by one.

Also, Read – 100+ Machine Learning Projects Solved and Explained.

Decision Tree

A decision tree is an algorithm that predicts the label associated with an instance by travelling from a root node of a tree to a leaf. For example, we need to classify whether papaya is tasty or not, let’s see how the decision tree will express itself in this problem.

To classify whether the papaya is tasty or not, the decision tree algorithm will first check the colour of the papaya. If the colour is nowhere around pale green to pale yellow, then the algorithm will predict that papaya is not tasty without looking at more characteristics.

So what happens is we start from a tree with a single leaf and label it based on the majority of the labels we have in the learning set. Then we do a few iterations, with each iteration we see the effect of splitting the single sheet.

Then among all the possible number of splits, either we select the one which maximizes the gain, or we choose not to select the sheet at all. This is how the decision tree algorithm works. You can learn how to implement it using Python from here.

Naive Bayes

Naive Bayes is a powerful supervised machine learning algorithm used for classification problems. It uses features to predict a target variable. The difference between Naive Bayes and other classification algorithms is that Naive Bayes assumes that the features are independent of each other and that there is no correlation between the features.

So what happens is that this hypothesis is not evaluated based on real-life issues. So, this naive assumption that features are uncorrelated is why this algorithm is known as Naive Bayes.

This algorithm is a classic demonstration of how generative assumptions and parameter estimates can simplify the learning process. In the Naive Bayes algorithm, we make predictions by assuming that the given characteristics are independent of each other. You can learn the practical implementation of this algorithm using Python from here.

K Nearest Neighbor

K Nearest Neighbor is one of the simplest classification algorithms in machine learning. It works by learning from the training data, and then it predicts the label of any category based on the labels of its nearest neighbours in the training data.

It follows that the features used to describe the structure of the data points are most relevant to their labels in a way that brings them closer to the points to have the same label.

KNN is a very simple machine learning classification algorithm that is based on the assumption that items that look alike must be the same. We need all of the training set to be stored while training, and while testing the algorithm, we need to test the entire dataset to find the nearest neighbour. You can learn the practical implementation of KNN from here.

Support Vector Machine

The Support Vector Machine is a very powerful machine learning algorithm that can be used for both classification and regression tasks. But since SVM is used more in classification, we can say that it is also a classification algorithm.

The SVM algorithm is used to learn half-spaces with some knowledge of the large margin preference. There are two types of SVM; Hard-SVM, Soft-SVM. The Hard-SVM finds the half-space that perfectly divides the data with the greatest margin. Soft-SVM does not find any point of the split, it allows to violate the constraints to a certain extent.

You can learn the practical implementation of the Support Vector Machine algorithm with Python from here.

Logistic Regression

Logistic regression is a very useful classification algorithm in machine learning. Logistic regression is used to find a relationship between input features and output labels. But that’s what we do in regression problems, so how is logistic regression a classification algorithm?

The answer is that in the regression we find the value of the label but logistic regression is used to find the category of the label.

For example, if we want to predict a student’s grades then this is the regression problem, but if we want to predict whether the student will pass or fail the exam then it’s the classification problem where the logistic regression can be used.

You can learn the practical implementation of Logistic Regression algorithm with Python from here.


We have covered 5 main classification algorithms used in machine learning classification problems, namely:

  1. Decision Tree
  2. Naive Bayes
  3. K Nearest Neighbors
  4. Support Vector Machines
  5. Logistic Regression

More algorithms can be used for classification, but they can be just used, they are just not intended for classification only. Hope you liked this article on classification algorithms in machine learning. Please feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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