Regression algorithms can be described as a subset of machine learning (ML) algorithms that predict a continued output variable that depends on input features which can be several or just one. Regression aims to model the relationship of the outputs – the dependent variable – and the input/s – the independent variable/s. Data scientists use these algorithms to find the best-fit line, surface, or curve which minimizes the differences between the actual and predicted values.
In this article, we will talk about the leading types of machine learning regression algorithm and provide you with all the details that will help you understand them better as a student.
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5 Types of Machine Learning Regression Algorithm
1. Linear Regression
Definition: Linear regression machine learning is used for supervised learning and predicts the target – the dependable variable – depending on the independent variable/s. There are two variations of the Linear regression algorithm – the simple linear regression and the multiple linear regression in machine learning.
Pros: It has several advantages like ease of implementation and a lower level of complexity compared to other algorithms. It might lead to over-fitting but you can always avoid that by using dimensionality reduction techniques.
Cons: The algorithm is affected severely by outliers and it also oversimplifies real-world issues by assuming that variables have a linear relationship. Thus it is not a good option for practical cases.
2. Decision Tree Regression
Definition: Tree models are used in all data that contains categorical and numerical features. Such a type of regression in machine learning is great at finding out the non-linear interaction that the target variable has with the features.
Pros: The Decision Tree model is visually intuitive apart from being easy to interpret and understand. It works with various categorical and numerical features.
Cons: It has a tendency of over-fitting and even a minor change in data might lead to a major difference in the tree structure thus leading to instability.
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3. Support Vector Regression
Definition: One of the top types of regression models, the Support Vector regression model works the same way as the Support Vector Machine with the only difference being that the Support Vector regression model aims to forecast the actual values.
Pros: The major advantages of the Support Vector regression model are that it stands firm even when faced with outliers, has high prediction accuracy, and offers exceptional generalization capability.
Cons: The biggest issue with the Support Vector regression model is that it is not recommended when working with large datasets. It also does not perform well in cases where the data sets are noisier.
4. Lasso Regression
Definition: Lasso (Least Absolute Selection Shrinkage Operator) is one of the simplest types of regression models. This is a variable and shrinkage selection method that looks to determine the most important predictors in any data set – it is also referred to as a classification algorithm.
Pro: The biggest advantage of the Lasso regression model is that it never over-fits.
Cons: The Lasso regression model has a couple of issues. For starters, it selects only one feature from a group of features that are correlated. It also does not help that the selection features in these cases tend to be significantly biased.
5. Random Forest Regressor
Definition: Random Forests are defined as combinations or ensembles of Decision Trees. This is one of the most prominent types of regression in machine learning. This supervised learning algorithm is used for regression and classification.
Pros: The Random Forest Regressor model is good when it comes to learning non-linear and complex relationships. It is also rather easy to understand and interpret.
Cons: This model is however rather prone to over-fitting. If you employ large Random Forest ensembles for achieving higher performance it will slow them down and need more memory as well.
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Conclusion
Machine learning regression algorithm is only a part of Data Science which has become such an important educational discipline these days thanks to factors such as high demand for data scientists, versatility, competitive edge, cutting-edge curriculum, and networking experience.
If you want to take up masters in AI and ML in India you must always choose courses which offer you hands-on experience, global perspective and exposure, and job security.
You can visit the official website of Amity and explore its top online programs such as the MCA in Artificial Intelligence program.
