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Beyond the Basics - Advanced Model Evaluation Metrics

Beyond the Basics - Advanced Model Evaluation Metrics - SQL教程

课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced

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Beyond the Basics - Advanced Model Evaluation Metrics

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- [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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