10 Popular Regression Algorithms in Machine Learning

The global Machine Learning market is expected to reach USD 117 billion by 2027 with an impressive CAGR (Compound Annual Growth Rate) of 39%.  

That sounds like it. Nowadays Machine Learning (ML) has a wide range of industrial applications that are likely to increase in the coming areas. Freshers and tech enthusiasts should know about Machine Learning concepts to upskill and build a successful career in the ML industry. Regression algorithms in Machine Learning are an important concept with a lot of use cases.  

Regression algorithms play a central requirement in mapping out patterns, making estimates, and steering conclusions backed by data. With an extensive range of algorithms at our disposal, each with its unique strengths, assumptions, and applications, analyzing their differences is pivotal for opting to implement the ideal tool for the task.  

This article explores ten of the most popular regression algorithms in machine learning, offering a comparative glimpse into how they work, where they excel, and why they matter in real-world scenarios.  

You might like: Machine learning methods – An overview 

What is Regression in Machine Learning? 

Regression in machine learning is a structed learning approach conceptualized to predict continuous, numerical outputs based on input data. It functions by pinpointing relationships between independent variables—such as features like age, income, or product demand—and a target variable, like price or performance. With the assistance of historical data, regression models, we put together the ability to guesstimate values for new, untapped dimensions.  

This makes them profoundly useful in any field where numbers and trends play a role. Whether it’s a basic linear regression or an advanced model like Ridge or Lasso, each algorithm intends to get the hold of patterns that guide consistent, data-backed predictions. Businesses use regression to forecast revenue, doctors use it to anticipate patient health outcomes, and analysts rely on it to model market behavior.  

Applications of Regression 

Regression is a staple in multiple sectors to model relationships between variables and make definite results. Here are some key applications that point out its versatility and essentiality.  

Predictive modeling 

Regression is broadly implemented to forecast future values based on historic data. For instance, businesses estimate sales volumes or stock prices by assessing market dynamics and behavior. This facilitates planning and decision-making. Precise predictions upgrade operational approaches and resource allocation. 

Risk assessment 

In finance industry, regression models assess the possibility of threats such as broken loan agreements or coverage claims. By observing historical data, these models analyze the probability and consequence of risks. This guides institutions to set relevant interest rates or premiums. It enriches risk management and reduces the likelihood of potential losses.  

Marketing analysis 

Regression helps marketers interpret how alternate factors like campaign costs or pricing affect sales figures. It measures the association between marketing activities and buyer response. This clarity steers budget allocation and campaign optimization. Therefore, businesses can increase sales successfully. 

Healthcare 

In healthcare, regression predicts recovery status based on medical factors like age, symptoms, or treatment types. It supports doctors to consider disease progression and treatment effectiveness. This leads to customized care plans and advanced patient management. In the long run, it enables better healthcare decisions. 

Real estate 

Regression models assess land values by analyzing aspects such as location, size, and amenities. Real estate experts apply these models to set reasonable prices and forecast industry shifts. Buyers and sellers upside from more trusted financial reports for the same.  This surges straightforwardness and faith in property transactions. 

Demand forecasting 

Businesses employ regression to predict product demand based on historical sales and other variables like seasonality or promotions. This supports inventory management and supply chain optimization. Accurate forecasts lessen stockouts and overstock scenarios. It confirms seamless operations and end satisfaction. 

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Types of Regression Algorithms 

Different types of regression techniques assist in modeling the relationship between variables in multiple ways, each relevant for specific kinds of data and problem scenarios. Below are a few:  

1) Linear Regression 

It is one of the most-used regression algorithms in Machine Learning. A significant variable from the data set is chosen to predict the output variables (future values). Linear regression algorithm is used if the labels are continuous, like the number of flights daily from an airport, etc. The representation of linear regression is y = b*x + c. 

In the above representation, ‘y’ is the independent variable, whereas ‘x’ is the dependent variable. When you plot the linear regression, then the slope of the line that provides us the output variables is termed ‘b’, and ‘c’ is its intercept. The linear regression algorithms assume that there is a linear relationship between the input and the output. If the dependent and independent variables are not plotted on the same line in linear regression, then there will be a loss in output. The loss in output in linear regression can be calculated as: 

Loss function: (Predicted output – actual output)². 

2) Ridge Regression 

Ridge Regression is another popularly used linear regression algorithm in Machine Learning. If only one independent variable is being used to predict the output, it will be termed as a linear regression ML algorithm. ML experts prefer Ridge regression as it minimizes the loss encountered in linear regression (discussed above). In place of OLS (Ordinary Least Squares), the output values are predicted by a ridge estimator in ridge regression. The above-discussed linear regression uses OLS to predict the output values.  

The complexity of the ML model can also be reduced via ridge regression. One should note that not all the coefficients are reduced in ridge regression, but it reduces the coefficients to a greater extent as compared to other models. The ridge regression is represented as:  

y = Xβ + ϵ,  

where ‘y’ is the N*1 vector defining the observations of the dependent data point/variable and ‘X’ is the matrix of regressors. ‘β’ is the N*1 vector consisting of regression coefficients and ‘ϵ’ is the vector (N*1) of errors. The ridge algorithm is also used for regression in Data Mining by IT experts besides ML.  

3) Neural Network Regression  

You all must be aware of the power of neural networks in making predictions/assumptions. Each node in a neural network has a respective activation function that defines the output of the node based on a set of inputs. The last activation function can be manipulated to change a neural network into a regression model. One can use ‘Keras’ that is the appropriate python library for building neural networks in ML.  

The output of a neuron is mapped to a variety of values in neural network regression, thus ensuring non-linearity. You can choose a single parameter or a range of parameters for predicting output using neural network regression. The neurons (outputs of a neural network are well-connected with each other, along with a weight associated with each neuron. The well-connected neurons help in predicting future values along with mapping a relationship between dependent and independent variables.  

4) Lasso Regression  

Lasso (Least Absolute Shrinkage and Selection Operator) regression is another widely used linear ML regression (one input variable). The sum of coefficient values is penalized in lasso regression to avoid prediction errors. The determination coefficients in lasso regression are reduced towards zero by using the technique ‘shrinkage’. The regression coefficients are reduced by lasso regression to make them fit perfectly with various datasets. Besides ML, the lasso algorithm is also used for regression in Data Mining. 

ML experts opt for the lasso regression algorithm when there is high multicollinearity in the given dataset. Multicollinearity in the dataset means independent variables are highly related to each other, and a small change in the data can cause a large change in the regression coefficients. Lasso algorithm regression can be used in predicting forecasting applications in ML.  

5) Decision Tree Regression  

Non-linear regression in Machine Learning can be done with the help of decision tree regression. The main function of the decision tree regression algorithm is to split the dataset into smaller sets. The subsets of the dataset are created to plot the value of any data point that connects to the problem statement. The splitting of the data set by this algorithm results in a decision tree that has decision and leaf nodes. ML experts prefer this model in cases where there is not enough change in the data set.  

One should know that even a slight change in the data can cause a major change in the structure of the subsequent decision tree. One should also not prune the decision tree regressors too much as there will not be enough end nodes left to make the prediction. To have multiple end nodes (regression output values), one should not prune the decision tree regressors excessively. 

6) Random Forest  

Random forest is also a widely-used algorithm for non-linear regression in Machine Learning. Unlike decision tree regression (single tree), a random forest uses multiple decision trees for predicting the output. Random data points are selected from the given dataset (say k data points are selected), and a decision tree is built with them via this algorithm. Several decision trees are then modeled that predict the value of any new data point.  

Since there are multiple decision trees, multiple output values will be predicted via a random forest algorithm. You have to find the average of all the predicted values for a new data point to compute the final output. The only drawback of using a random forest algorithm is that it requires more input in terms of training. This happens due to the large number of decision trees mapped under this algorithm, as it requires more computational power.  

7) KNN Model  

KNN model is popularly used for non-linear regression in Machine Learning. KNN (K Nearest Neighbours) follows an easy implementation approach for non-linear regression in Machine Learning. KNN assumes that the new data point is similar to the existing data points. The new data point is compared to the existing categories and is placed under a relatable category. The average value of the k nearest neighbors is taken as the input in this algorithm. The neighbors in KNN models are given a particular weight that defines their contribution to the average value.  

A common practice of assigning weights to neighbors in a KNN model is 1/d, where d is the distance of the neighbor from the object whose value is to be predicted. In determining the value of a new data point via the KNN model, one should know that the nearest neighbors will contribute more than the distant neighbors. 

8) Support Vector Machines (SVM) 

SVM can be placed under both linear and non-linear types of regression in ML. The use cases of SVM can range from image processing and segmentation, predicting stock market patterns, text categorization, etc. When you have to identify the output in a multidimensional space, the SVM algorithm is used. In a multidimensional space, the data points are not represented as a point in a 2D plot. The data points are represented as a vector in a multidimensional space.  

A max-margin hyperplane is created under this model that separates the classes and assigns a value to each class. Freshers should know that an SVM model does not perform to its fullest extent when the dataset has more noise. 

9. Gaussian Regression 

Gaussian regression algorithms are commonly used in machine learning applications due to their representation flexibility and inherent uncertainty measures over predictions. A Gaussian process is built on fundamental concepts such as multivariate normal distribution, non-parametric models, kernels, joint and conditional probability. 

A Gaussian processes regression (GPR) model can predict using prior knowledge (kernels) and provide uncertainty measures for those predictions. It is a supervised learning method developed by computer science and statistics communities. 

Due to the nonparametric nature of Gaussian process regression, it is not constrained by any functional form. As a result, instead of calculating the probability distribution of a specific function’s parameters, GPR computes the probability distribution of all permissible functions that fit the data.  

10. Polynomial Regression 

Polynomial Regression is a regression algorithm that models the relationship between an independent variable (x) and a dependent variable (y) as an nth degree polynomial. The equation for Polynomial Regression is as follows: 

y= b0 b1x1 b2x12 b2x13 …… bnx1n 

It is also known as the special scenario of Multiple Linear Regression in machine learning. Because to make it polynomial regression, some polynomial terms are added to the Multiple Linear Regression equation. It is a linear model that has been modified to improve accuracy. The dataset used for training in polynomial regression is non-linear. To fit the non-linear and complicated functions and datasets. The original features are changed into Polynomial features of the required degree (2,3,…,n) and then modelled using a linear model. 

Also read: Machine learning career path 

How to Choose the Right Regression Algorithm? 

Choosing the right regression algorithm depends on the nature of your data and the problem you’re solving. Below is the step-by-step guide for the same.  

Understand your data type and size 

Selecting the apt regression algorithm begins with assessing your data type—whether it’s continuous, categorical, or a mix. The capacity of your data set also matters; some algorithms function better with large datasets, while others fit the best for smaller samples. Understanding these aspects helps narrow down suitable models efficiently. 

Check for linearity vs. nonlinearity 

Understanding whether your data exhibits a linear or nonlinear relationship is crucial. Linear regression works well for straightforward trends, while nonlinear models like polynomial or decision tree regressions handle complex patterns. Picking the right approach ensures your model captures the true underlying relationship. 

Model complexity and Interpretability 

Balancing model complexity with interpretability is key; simpler models are easier to explain but might miss subtle patterns. More complex models can improve accuracy but often sacrifice transparency. Consider your audience and the importance of explaining model decisions when choosing. 

Avoiding overfitting 

Overfitting occurs when a model learns noise instead of the underlying pattern, leading to poor generalization. Selecting algorithms with built-in regularization or using techniques like cross-validation can help. The goal is to build a model that performs well on both training and unseen data. 

Performance metrics matter 

Different regression algorithms are evaluated using metrics like RMSE, MAE, or R². Choosing the right metric depends on your business goals and the nature of your predictions. Monitoring these metrics helps identify which algorithm delivers the best balance of accuracy and reliability. 

Computational resources and time 

Some regression algorithms require significant computational power and time, especially with large datasets or complex models. Consider your available resources and deadlines when selecting an algorithm. Efficient algorithms can save time without compromising too much on accuracy. 

Domain knowledge and business needs 

Integrating domain expertise ensures the chosen regression model aligns with real-world business problems. Understanding what drives your data helps in selecting features and interpreting results effectively. The right algorithm supports actionable insights tailored to your specific goals. 

Advantages and Disadvantages of Regression Algorithms 

Advantages	Disadvantages
Simple to understand and interpret	Can underperform with complex or nonlinear data
Effective for predicting continuous outcomes	Sensitive to outliers that can skew results
Computationally efficient for small to medium data	May overfit with high-dimensional or noisy data
Provides insight into relationships between variables	Assumes linearity in basic models, limiting use
Widely used and supported by many tools	Requires careful feature selection and preprocessing
Can handle multiple input variables	Performance depends heavily on data quality

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Conclusion 

These were some of the top algorithms used for regression analysis. Fresher or not, you should also be aware of all the types of regression analysis. You should also identify the number of variables you are going to use for making predictions in ML. If you have to use only one independent variable for prediction, then opt for a linear regression algorithm in ML. 

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FAQs  

What are regression algorithms in machine learning? 

In machine learning, regression methods are used to forecast continuous numerical values from input data.

How many regression algorithms are there? 

In machine learning, there are roughly ten to fifteen popular regression algorithms.  There are twelve main categories that are commonly used in real-world situations, while the precise number can change based on how they are grouped or mixed. 

What are the 4 types of machine learning algorithms? 

The 4 types of machine learning algorithm are  

• Supervised Learning 

• Unsupervised Learning 

• Semi-Supervised Learning 

• Reinforcement Learning 

What is CNN in machine learning? 

In machine learning, CNNs (Convolutional Neural Networks) are a kind of deep learning algorithm that is mostly used for processing and evaluating visual input, such as pictures and movies.  

What is the formula for regression in ML? 

A simple linear regression has an equation of the form Y = b0+ b1*x1, where x1 is the predictor and Y is the dependent variable.