Most Famous Supervised Machine Learning Algorithms

Supervised learning is a process of providing input data as well as correct output data to the machine learning model.

Most Famous Supervised Machine Learning Algorithms
  • Supervised learning is the type of machine learning in which machines are trained using well “labeled” training data, and on basis of that data, machines predict the output.

  • The labeled data means some input data is already tagged with the correct output.

  • The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).

  • Supervised learning can be used for Risk Assessment, Image classification, Fraud Detection, Spam Filtering, etc.

Types of Supervised Machine learning Algorithms:

1. Regression

Regression algorithms are used if there is a relationship between the input variable and the output variable.

2. Classification

Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc.

Most Famous Supervised Learning Algorithms:

1. Linear Regression

  • Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable’s value is called the independent variable.

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2. Logistic Regression

  • This type of statistical model (also known as the logit model) is often used for classification and predictive analytics. Logistic regression estimates the probability of an event occurring, such as voted or didn’t vote, based on a given dataset of independent variables.

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3. K-nearest Neighbors

  • The k-nearest neighbor’s algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.

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4. Support Vector Machine (SVM)

  • Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.

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5. Decision Tree

  • A decision tree builds regression or classification 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 final result is a tree with decision nodes and leaf nodes.

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6. Gradient Boosting

  • Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. The key idea is to set the target outcomes for this next model in order to minimize the error.

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