From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced
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Beyond the Basics - Advanced Model Evaluation Metrics - SQL Tutorial
From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced
Beyond the Basics - Advanced Model Evaluation Metrics
- [Instructor] Hats off to you on what you've accomplished this far. You've explored model diagnostics, coefficient interpretation, the nuances of polynomial and interaction terms, lasso and ridge and now, it's time to elevate your toolkit with advanced model evaluation metrics. As you've learned, R-squared has been your go-to statistic for assessing how well your models predict skater performance but as any seasoned skater knows, perfecting the part requires more than just one trick. Similarly, truly understanding your model's effectiveness calls for a broader set of evaluation metrics. Think of R-squared as a measure of how much the variance in skater performance your model can explain. However, adding more predictors to a model can inflate this statistic even if those predictors don't truly enhance the model's accuracy. Enter adjusted R-squared. Picture adjusted R-squared as a more discerning judge at a skate competition adjusting scores to consider the number of tricks attempted…
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Contents
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Model Diagnostics and Assumption Testing5m 18s
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Unlocking the Secrets Behind Coefficients3m 55s
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Polynomial Paths and Interactions3m 33s
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Striking a Balance with Regularization Techniques3m 34s
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Beyond the Basics - Advanced Model Evaluation Metrics3m 58s
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Non-parametric Regression Methods4m 24s
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Case Studies in Regression Analysis3m 3s
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