It is very crucial for the machine learning enthusiasts to know and understands the basic and important machine learning algorithms in order to keep themselves up with the current trend. In this article, we list down 10 basic algorithms which play very important roles in the machine learning era.
Logistic regression, also known as the logit classifier is a popular mathematical modelling procedure used in the analysis of data. Regression Analysis is used to conduct when the dependent variable is binary i.e. 0 and 1. In Logistic regression, logistic function is used to describe the mathematical form on which the logistic model is based. The reason behind the popularity of the logistic model is that the logistic function estimates that the variable must lie between 0 and 1.
K-Nearest Neighbours is one of the most essential classification algorithms. It is also known as the lazy learning as the function is only approximated locally and all the computations are deferred until classification. The algorithm selects the k nearest training samples for a test sample and then predicts the test sample with the major class amongst k nearest training samples.
This simple classification algorithm is based on the Bayes Theorem. The algorithm aims to calculate the conditional probability of an object with a feature vector which belongs to a particular class. It is called “Naive” because it makes the assumption that the occurrence of a certain feature is independent of the occurrence of other feature.
Support Vector Machine is a supervised learning technique which represents the datasets as points. The main goal of SVM is to construct a hyperplane which divides the datasets into different categories and the hyperplane should be at the maximum margin from the various categories. This algorithm helps in removing the over-fitting nature of the samples and provides better accuracy.
Random Forests are basically the combination of tree predictors where each tree depends on the values of a random vector that are sampled independently and with the same distribution for all the trees in the forest. This technique is easy to use as well as flexible because it can be both used for classification and regression tasks.
