10 Regression Algorithms You Need to Know for Machine Learning

Exploring the top 10 regression algorithms for machine learning expertise in 2024

In the subject of machine learning, it is essential to comprehend regression algorithms. Ten fundamental regression algorithms are introduced in this tutorial, which serves as the foundation for many machine learning models. There are many different Machine Learning Use Cases for these algorithms, ranging from evaluating medical data to forecasting sales.

To assist you in using these algorithms in practical situations, the ebook also offers Machine Learning Proof of Concept Ideas. Regardless of your level of experience, this tutorial will improve your understanding of machine learning. Here are 10 regression algorithms that you should know for machine learning:

• Linear regression: An approach that's easy to utilize and assumes that the input and output variables have a linear relationship. The straight line that fits the data the best and minimizes the sum of squared errors between the observed and anticipated values is found.

• Logistic regression: A binary classification method that forecasts the likelihood of an input falling into one of two categories, like spam or not. The final choice is made by applying a threshold after the input has been mapped to a value between 0 and 1 using a logistic function.

• Ridge regression: A linear regression variant in which the cost function has an additional regularization term that penalizes big coefficients and lessens overfitting. When there is a high correlation between the input variables or when there are more input variables than observations, it is helpful.

• Lasso regression: Another kind of linear regression that does feature selection and includes a regularization factor in the cost function, but with a different penalty that tends to reduce some coefficients to zero. It comes particularly handy when there are a lot of unimportant characteristics or sparse input variables.

• Elastic net: The use of a weighted sum of the regularization factors in both ridge and lasso regression. Both correlated and sparse input variables may be handled, and it strikes a compromise between variance and bias.

• Polynomial regression: A polynomial function of a specific degree that is fitted to the input and output variables as an extension of linear regression. Though it might be susceptible to overfitting and excessive computing costs, it can also capture complex patterns and non-linear correlations in the data.

• Support vector regression: Support vector machines are strong classification algorithms that employ kernels to transfer the input to a higher-dimensional space and identify the ideal hyperplane that divides the classes. This is a regression version of the algorithm. Kernels are also used in support vector regression to identify the best-fitting function with the least amount of divergence from the observed values within a certain range.

• Decision tree regression: An algorithm based on trees that divides the input space into smaller sections according to a set of rules and gives each sector a fixed output value. In addition to being simple to understand and capable of handling both category and numerical input variables, it has the potential to be unstable and overfit.

• Random forest regression: An ensemble method that takes the average of the predictions from many decision trees. It can deal with outliers and missing variables and lowers variance while enhancing the accuracy of a single decision tree. This regression approach is widely used and highly efficient.

• Neural network regression: An algorithm for deep learning, comprising several layers of synthetic neurons, can comprehend intricate and non-linear correlations between the input and output variables. It needs a lot of data, processing, and adjustment, but it is also incredibly powerful and versatile.