Regression with SDCA | Evaluation Metrics | ML.Net скачать в хорошем качестве

Regression with SDCA | Evaluation Metrics | ML.Net

In this training session, we explore Supervised Machine Learning for Regression using the Stochastic Dual Coordinate Ascent (SDCA) algorithm and break down how to evaluate model performance using key metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² (Coefficient of Determination). This session is ideal for developers, data professionals, and students who want a practical, easy-to-understand introduction to regression modeling and model evaluation in real-world ML projects. https://github.com/kmrkaushal/ml.net 📚 What You’ll Learn ✔ What supervised learning is and where regression fits ✔ How the SDCA algorithm works for regression ✔ Building models using historical labeled data ✔ Understanding prediction accuracy and model reliability ✔ Why evaluation metrics matter in machine learning ✔ Interpreting: MAE – average absolute prediction error RMSE – penalizing large errors R² – how well the model explains variation ✔ Common challenges like overfitting and data quality ✔ Tips for improving regression model performance Key Concepts Covered Supervised Learning Regression Models SDCA Algorithm Error Minimization Evaluation Metrics (MAE, RMSE, R²) #MachineLearning #SupervisedLearning #RegressionAnalysis #SDCA #MLNet #CSharp #ModelEvaluation #MAE #RMSE #R2Score #AIForDevelopers