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Five Types of Classification Algorithms in Data Science

Five Types of Classification Algorithms in Data Science

What Is Classification Technique?

Classification is a technique in data science used by data scientists to categorize data into a given number of classes. This technique can be performed on structured or unstructured data and its main goal is to identify the category or class to which a new data will fall under.

This technique also has its algorithms that can be used to enable text analysis software to perform tasks such as analyzing aspect-based sentiment and categorize unstructured text by topic and polarity of opinion. There are five classification algorithms that mostly used in data science as we will discuss later.

Neural Network

First, there is neural network. It is a set of algorithms that attempt to identify the underlying relationships in a data set through a process that mimics how human brain operates. In data science, neural networks help to cluster and classify complex relationship. Neural networks could be used to group unlabelled data according to similarities among the example inputs and classify data when they have a labelled dataset to train on.

K-Nearest Neighbors

KNN (K-Nearest Neighbors) becomes one of many algorithms used in data mining and machine learning, KNN is a classifier algorithm in which the learning is based on the similarity of data (a vector) from others. It also could be used to store all available cases and classifies new cases based on a similarity measure (e.g., distance functions).

Decision Tree

Decision tree algorithm is included in supervised learning algorithms. This algorithm could be used to solve regression and other classification problems. Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The purpose of using decision tree algorithm is to predict class or value of target variable by learning simple decision rules concluded from prior data.

Random Forest

Random forests are an ensemble learning method for classification, regression and other tasks that operates by constructing multiple decision trees at training time. For classification task, the output from the random forest is the class selected by most trees. For the regression task, the mean or mean prediction of each tree is returned. Random forests generally outperform decision trees but have lower accuracy than gradient boosted trees. However, the characteristics of the data can affect its performance.

Naïve Bayes

Naive Bayes is a classification technique based on Bayes' theorem with the assumption of independence between predictors. In simple terms, the Naive Bayes classifier assumes that the presence of certain features in a class is not related to the presence of other features. Classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. It's updating knowledge step by step with new info.

Conclusion

Classification algorithms in machine learning use input training data to predict the likelihood that subsequent data will fall into one of the predetermined categories. There are five classification algorithms that have been widely used in data science that are broken down into Neural Network, K-Nearest Neighbors, Decision Tree, Random Forest, and Naïve Bayes.

Reference:
5 Types of Classification Algorithms in Machine Learning. (2020, August 26). MonkeyLearn Blog. https://monkeylearn.com/blog/classification-algorithms/
José, I. (2021, June 2). KNN (K-Nearest Neighbors) #1 - Towards Data Science. Medium. https://towardsdatascience.com/knn-k-nearest-neighbors-1-a4707b24bd1d
Neural Network Definition. (n.d.). Investopedia. Retrieved June 17, 2021, from https://www.investopedia.com/terms/n/neuralnetwork.asp

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