Top 5 Regression Algorithms for Data Scientists

Consider a scenario where you must forecast a company's sales revenue based on past sales data or the temperature for a specific day based on other climatic variables like humidity, wind speed, etc. Do these tasks seem incredibly challenging? What approach will you apply to generate these predictions? Well, if you're stuck for ideas, don't worry. Regression algorithms are here at your disposal! Regression algorithms identify correlations between dependent and independent variables. These algorithms thus help forecast continuous variables like housing values, market movements, weather patterns, oil and gas prices, etc. This blog explores the top five regression algorithms that are highly popular among data scientists.

1. Decision Tree Regression

Decision tree regression can be used to perform non-linear regression in machine learning. The main purpose of the decision tree regression algorithm is to divide the dataset into more manageable components. Any data point relevant to the problem statement can be plotted using the subsets of the dataset. This algorithm divides the data set, generating a decision tree with decision and leaf nodes. Data scientists prefer this approach when there is not enough variation in the data set. The structure of the resulting decision tree can be significantly altered by even a small change in the data. Additionally, if the decision tree regressors are reduced too much, there won't be enough end nodes left to make the forecast.

2. Linear Regression

Linear regression is a popular supervised learning ML approach suitable for individuals new to data science. Depending on the given independent variable, linear regression predicts a dependent variable (target). The linear regression technique assumes that the input and output relationship is linear. There will be a loss in output if the dependent and independent variables are not plotted on the same line in linear regression.

3. Lasso Regression

Another popular linear ML regression is the Lasso (Least Absolute Shrinkage and Selection Operator) regression. Lasso regression penalizes the total value of the coefficients to prevent prediction errors. In lasso regression, the shrinkage strategy minimizes the determination coefficients toward zero. Lasso regression minimizes the regression coefficients to ensure a perfect fit with different datasets.

4. Random Forest

Another popular approach for non-linear regression in machine learning is random forest. A random forest uses many decision trees to forecast the outcome unlike decision tree regression (single tree). With the help of this approach, a decision tree is created using 'k' randomly chosen data points from the given dataset. The value of every new data point is then predicted using several decision trees.

5. Gaussian Regression

Due to the flexibility of their representations and built-in metrics of prediction uncertainty, Gaussian regression methods are commonly used in machine learning applications. Fundamental concepts such as multivariate normal distribution, non-parametric models, kernels, joint, and conditional probability are the foundation of a Gaussian process. A Gaussian processes regression (GPR) model can make predictions based on previous information (kernels) and offer uncertainty metrics for those predictions.

Learning about these algorithms is not enough. You must try implementing these algorithms in some real-world use-case scenarios. Build a project from scratch or practice with some open-source projects from Github or Kaggle.

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