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What is Regularization? Keeping Models in Check
access_time 2025-04-26T11:22:33.911Z face Nerchuko
L1 vs L2 Regularization: Taming Complex Models Learn how Lasso (L1) and Ridge (L2) prevent overfitting and improve your models. What is Regularization? Keeping Models in Check Imagine training a machine learning model, like one predicting house prices. If the model gets too complex (maybe using too ...
Backward Elimination: Building Simpler, Smarter Models
access_time 2025-04-26T11:13:00.584Z face Nerchuko
Backward Elimination: Simplifying Your Regression Models Learn how to remove less useful features step-by-step using P-values and Adjusted R². Backward Elimination: Building Simpler, Smarter Models When building a Multiple Linear Regression model, we often start by including many potential input fea...
R² vs. Adjusted R²: Which Tells the Real Story?
access_time 2025-04-26T11:03:48.511Z face Nerchuko
R-Squared vs. Adjusted R-Squared: Which Metric to Trust? Understand how to evaluate your regression models accurately. R² vs. Adjusted R²: Which Tells the Real Story? When we build regression models to predict values, we need a way to measure how well they actually fit the data. Two of the most comm...
Measuring Success: How Good is Your Regression Model?
access_time 2025-04-26T10:57:42.77Z face Nerchuko
How Good is Your Regression Model? Understanding Key Metrics Learn to evaluate predictions using MAE, RMSE, R², and Adjusted R². Measuring Success: How Good is Your Regression Model? So you've built a regression model, perhaps using Simple Linear Regression, Multiple Linear Regression, or even a pow...
Random Forest Regression: Power in Numbers
access_time 2025-04-26T10:45:35.13Z face Nerchuko
Random Forest Regression Explained Unlock the power of many decision trees working together for accurate predictions. Random Forest Regression: Power in Numbers We've learned about Decision Trees for predicting numbers (Regression Trees). They are intuitive, like flowcharts. But sometimes, a single ...