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Classification Algorithms in Machine Learning

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  • September 29, 2021

If you are not very familiar with what role classification algorithms play and where, it is a part of the supervised machine learning algorithm. The supervised machine learning algorithm uses a training set to teach models to generate the desired output. It’s division is in two types.

The following are the types:

  • Regression Algorithms
  • Classification Algorithms
regression algorithm, classification algorithms

The regression algorithm helps in identifying the link in between dependent and independent variables. It is a popular tool for making projections. In the regression model, we predict the output for continuous values.

The classification model, on the other hand, uses an algorithm to precisely allocate test results into relevant groups. We require classification techniques to forecast categorical values . This article will focus on classification algorithms in machine learning.

What Is a Classification Algorithm?

Classification is the process of classifying a given collection of data into classes (or labels or categories). Classification of data works on both structured and unstructured data. The Classification algorithm is a Supervised Learning technique that uses training data to identify the category of fresh observations. What happens in the classification algorithm exactly is that a software learns from the provided dataset or observations and then classifies new observations into a number of classes. Therefore, the primary aim of this technique is to determine which class the new data will belong to.

Types of Classifiers

The following are the types of classifiers:


It’s a classification method in which a set of data is divided into two groups based on the assumption that there are only two possible groups to which the data can belong. For example, in response to the inquiry “Are you a robot?” a person can only say “yes” or “no,” not “maybe.” Therefore, this is a binary classifier that groups all “yes” responses into one category and all “no” responses into another.

Some topics that are linked with binary classifier are as follows:

  • Precision: It relates to a model’s ability to accurately analyze positive observations in the binary classification (Yes/No). Moreover, it is concerned with the frequency by which a positive value prediction proves to be correct. It is the percentage of relevant instances (true positives) among all the examples expected to belong to a specific class.
  • Recall: Recall is a metric which determines how “sensitive” a classifier is to finding positive cases. However, categorizing both outcomes as positive has the ability to control this metric. Recall is the percentage of instances predicted to belong to a class compared to all of the examples that really belong in the class.
  • F1 Score: It’s a weighted average of precision and recall, with 1 being the highest and 0 being the lowest. Precision and recall both contribute equally to the f1 score.


Multi-class classification is the task of categorizing data into different classes. This approach, unlike binary, does not presume that there are only two groups and does not limit itself to a specific number of classes. When you look at a newspaper, for instance, you’ll see numerous sections such as sports, entertainment, local news, worldwide news, and so on. This is exactly what multi-class classification is.


Multi-label classification is an expansion of the multiclass categorization system. It is the task of categorizing data into different classes. In multi-label tasks, each label represents a different classification task, although the tasks are related in some way. In simple words, if there are three classes, the data we wish to categorize must either fit into all three or none at all.


Imbalanced classification is a classification challenge in which the distribution of classes in the training dataset is uneven. For example, if 30% of your emails are classified as spam out of a total of 100%. Unbalanced categorization exists.

Some Real-Life Applications of Classification Algorithms

The following are some real-life applications of classification algorithms:

Spam Classification

Email applications use classification techniques to evaluate the chance of an email being unwanted spam. Therefore, The classification algorithm is responsible for removing spam emails from the inbox. However, spam classifiers must still undergo training since it occasionally puts legitimate emails into spam folders believing they are spam when they are not.

Product Categorization

The technique of multinomial classification Classifies items supplied by several sellers into the same category. This use aspect relates to ecommerce platforms.

Document Classification

The multinomial classification algorithm divides documents into distinct groups according to their data.

Types of Classification Algorithms

The types are as follows:

Logistic Regression

The logistic regression is used to forecast a binary outcome. The binary outcome is determined by analyzing independent factors, with the findings falling into one of two groups (eg. yes or no.) Although the independent variables can be numeric or categorical, the dependent variable is always categorical. It does classification based on the regression, sorting the dependent variable into one of two groups.

logistic regression function

The regression subjects to a logistic function to determine the likelihood of it falling into one of two categories.

Naive Bayes

The Naive Bayes algorithm determines whether or not a data point belongs in a certain category. It’s not a single algorithm, but rather a group of algorithms. All of the algorithms follow the same basic principle: each pair of characteristics to be categorized is independent of the others. The Naive Bayes model is simple to construct and is especially effective when dealing with big data sets.

The following equation expresses Bayes’ theorem mathematically:

Naive bayes


  • A and B are events
  • Y = class variable
  • X = dependent feature vector (of size n)

Decision Tree

The decision tree method classifies data in the form of a tree structure. A decision tree generates a set of rules that help in categorizing data given a set of attributes and their classes. It is easily understandable and is capable of dealing with both numerical and categorical data. It functions similarly to a flowchart.

Random Forest

The random forest algorithm is a decision tree modification. It creates a “random forest” by fitting a number of decision trees on various sub-samples of datasets and then fitting your fresh data into one of the trees. It controls over-fitting and improves the model’s prediction accuracy by using average. In most situations, the drop in over-fitting and the random forest classification are more accurate than decision trees.

K-Nearest Neighbors (KNN)

The k-nearest neighbors (KNN) method is extremely easy to execute. It helps in predicting the categorization of a new sample point by identifying data points that are split into various groups. It uses a similarity measure to classify new occurrences. The KNN method can solve both classification and regression issues, just like logistic regression. This algorithm is very effective when the training data is massive.

Support Vector Machines (SVM)

Support Vector Machine algorithm mainly helps in solving two group classification issues. It also helps with regression issues. It can handle both linear and nonlinear problems and is useful for a wide range of applications. The SVM technique divides the data into classes by drawing a line or hyperplane. It’s simply a representation of the training data as a series of points in space divided into categories. It is simple and efficient since it employs a subset of training points in the decision function. It transforms your data using the kernel trick, and then calculates an optimum boundary between the potential outputs based on these modifications.