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SVM Substitute Algos

by akshara

Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. It is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).
Elements in Supervised Learning : Classification uses an algorithm to accurately assign test data into specific categories. Regression is used to understand the relationship between dependent and independent variables. What is SVM? The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.

Support Vector Machine

Libraries that can be used for SVM are:

Other Classification Techniques are :

Libraries are listed for each classification technique.

Naive Bayes

Naive Bayes is a probabilistic classifier, which means it predicts on the basis of the probability of an object. It is mainly used in text classification that includes a high-dimensional training dataset.

K-Nearest Neighbor

K-NN algorithm assumes the similarity between the new data and available data and put the new data into the category that is most similar to the available categories. K-NN algorithm stores all the available data and classifies a new data point based on the similarity.

Random Forest

Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.

Decision Tree

Decision Tree is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.