Regression in Machine Learning

Introduction

In machine learning, regression refers to a supervised learning technique used for predicting continuous numerical values based on input features. It's commonly employed when the target variable (the variable we're trying to predict) is quantitative or continuous in nature.

Regression models

Regression models establish relationships between input variables (features) and the target variable by learning from training data. The goal is to build a mathematical function that accurately maps input features to the target variable, enabling predictions for new or unseen data points.

Common usage of Regression

Regression finds extensive application across various domains, including.

• Finance: Predicting stock prices, forecasting market trends, and analyzing risk factors.
• Healthcare: Estimating patient recovery times, predicting disease progression, and assessing treatment effectiveness.
• Marketing: Forecasting sales volumes, analyzing customer behavior, and optimizing advertising strategies.
• Real Estate: Predicting property prices, assessing market demand, and analyzing housing trends.

Understanding Regression Metrics

1. Mean Absolute Error (MAE)

   • MAE quantifies the average absolute difference between predicted and actual values.
   • Formula: MAE = (1/n) * Σ |y_i - ŷ_i|

2. Mean Squared Error (MSE)

   • MSE calculates the average squared difference between predicted and actual values.
   • Formula: MSE = (1/n) * Σ (y_i - ŷ_i)^2

3. Root Mean Squared Error (RMSE)

   • RMSE, the square root of MSE, provides a measure in the same units as the target variable.
   • Formula: RMSE = √(MSE)

4. Coefficient of Determination (R²)

   • R² indicates the proportion of variance in the dependent variable explained by the independent variables.
   • Formula: R² = 1 - (SS_residual / SS_total)
   • SS_residual and SS_total represent the sums of squares of residuals and total sums of squares, respectively

Conclusion

Understanding regression metrics is pivotal for assessing model performance and guiding decision-making processes across various domains. By leveraging these metrics and their real-life applications, data practitioners can refine their models, identify areas for enhancement, and optimize predictive capabilities in regression tasks.

As the field of machine learning continues to evolve, a nuanced understanding of regression metrics empowers practitioners to extract meaningful insights, drive informed decisions, and unlock the full potential of predictive modeling in diverse industries and applications.