7.4 Principles of Supervised Learning: Regression Algorithms
Supervised learning is one of the most important categories of Machine Learning, where the objective is to learn a function that maps an input to an output based on examples of input-output pairs. Among the most common tasks in this category, we find regression, which aims to predict continuous values. Let's explore the most used regression principles and algorithms in the context of Machine Learning and Deep Learning with Python.
Basic Concepts of Regression
Regression seeks to establish the relationship between independent variables (or predictors) and a dependent variable (or target), modeling the expectation of one variable in terms of another(s). In Machine Learning, regression is used to predict continuous numerical values, such as house prices, temperatures, sales, among others.
Regression models are evaluated based on how well their predictions align with actual data. Metrics such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and the Coefficient of Determination (R²) are commonly used for this evaluation.
Regression Algorithms
There are several regression algorithms, and each one has its particularities and use cases. Let's discuss some of the most popular ones:
Linear Regression
Linear regression is one of the simplest and most widely used methods. It assumes that there is a linear relationship between the independent variables and the dependent variable. Linear regression can be simple (with one independent variable) or multiple (with several independent variables).
In Python, the scikit-learn library provides an efficient implementation of linear regression, which can be easily used to train and evaluate models.
Polynomial Regression
Polynomial regression is a form of linear regression where the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of degree n. This allows you to capture non-linear relationships between variables.
Ridge Regression (L2)
Ridge regression is a technique used when data presents multicollinearity (high correlation between independent variables). It adds a penalty term (L2 regularization) to the MSE to prevent overfitting.
Lasso Regression (L1)
Lasso regression also adds a penalty term to the MSE, but uses the L1 norm, which has the property of producing solutions where some of the regression coefficients are exactly zero, meaning that the corresponding variable is excluded from the model. This can be useful for feature selection.
Elastic Net Regression
Elastic Net regression combines L1 and L2 penalties. It is useful when there are several features that are correlated with each other, as it combines the feature selection properties of Lasso with the ability to model multicollinear data of Ridge.
Decision Trees for Regression
Decision trees can also be used for regression problems. They divide the feature space into distinct regions, and for each region, a prediction value is assigned based on the average of the target values within it.
Random Forest for Regression
Random Forest is an ensemble method that uses multiple decision trees to improve model robustness and performance. Each tree is trained with a sample of the data and makes an independent prediction. The final prediction is made by averaging the predictions of all trees.
Regression with Neural Networks
Artificial neural networks, including deep learning, can be applied to regression problems. They are capable of modeling complex and non-linear relationships between variables. In Python, libraries like TensorFlow and Keras make it easy to build and train neural networks for regression.
Implementation in Python
Python is an extremely popular programming language in the area of Machine Learning and Deep Learning, due to its simplicity and the large number of libraries available. To implement regression algorithms, we can use the scikit-learn library, which provides simple and efficient implementations of various Machine Learning algorithms.
In addition, for more complex tasks and deep learning models, we can turn to libraries such as TensorFlow and Keras, which offer greater flexibility and computational power to deal with large data sets and complex network architectures.
Conclusion
Regression algorithms are powerful toolsin the Machine Learning arsenal and are fundamental for predicting continuous values. Understanding supervised learning principles and the ability to implement and tune different regression algorithms are valuable skills for any data scientist or machine learning engineer. With practice and experience, it is possible to choose the most appropriate algorithm for each specific problem and achieve impressive results.
