Understanding regression algorithms is critical to the field of machine learning. This lesson introduces ten basic regression methods that form the basis of many machine learning models. These algorithms have a wide range of Machine Learning Use Cases, from predicting sales to analyzing medical data.
Along with Machine Learning Proof of Concept Ideas, the booklet will help you use these algorithms in real-world scenarios. No matter how much experience you have, this lesson will help you grasp machine learning better. The following ten regression algorithms are essential knowledge for machine learning:
Linear regression: An strategy that is simple to use and presupposes a linear relationship between the input and output variables is called linear regression. The best-fitting straight line is identified by minimizing the sum of squared errors between the observed and predicted values.
Logistic regression: A binary classification technique called logistic regression predicts the probability that an input will fall into one of two categories, such as spam or not. Once the input has been mapped using a logistic function to a value between 0 and 1, the final decision is made by applying a threshold.
Ridge regression: It is a variation on linear regression where a regularization factor is added to the cost function to punish large coefficients and reduce overfitting. It is useful when there is more input than observations or when there is a significant correlation between the input variables.
Lasso regression: Another type of linear regression called Lasso regression selects features and adds a regularization component to the cost function. However, it uses a different penalty that tends to drive some coefficients to zero. It is especially useful in situations where the input variables are sparse or there are many insignificant qualities.
Elastic net: Both ridge and lasso regression employ a weighted sum of the regularization components. It achieves a balance between variance and bias and can handle both sparse and correlated input variables.
Polynomial regression: As an extension of linear regression, polynomial regression involves fitting a polynomial function of a given degree to the input and output variables. It can capture complicated patterns and non-linear correlations in the data, but it may also be prone to overfitting and exorbitant computing expenses.
Support vector regression: Strong classification techniques known as support vector machines use kernels to move the input into a higher-dimensional space and choose the best hyperplane to split the classes along. This algorithm is a regression version. In support vector regression, kernels are also utilized to find the best-fitting function that deviates from the observed values within a certain range the least.
Decision tree regression: It is a tree-based technique that assigns a fixed output value to each sector after segmenting the input space into smaller parts based on predetermined rules. It can handle both category and numerical input variables and is easy to understand, but it also has the potential to be unstable and overfit.
Random forest regression: An ensemble technique called random forest regression averages the predictions made by numerous decision trees. It reduces variance and improves the accuracy of a single decision tree while handling outliers and missing information. Regression analysis is a popular and very effective technique.
Neural networks regression: This deep learning technique, which consists of multiple layers of artificial neurons, is able to understand complex and nonlinear relationships between the input and output variables. It is extremely strong and versatile, but it also requires a lot of data, processing, and tuning.
