课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
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Applying cross-validation and evaluation - SQL教程
课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
Applying cross-validation and evaluation
- Welcome to the 20th step of your journey. Did you hear that? 20. Now if this moment doesn't deserve a pause of recognition, then I don't know what does. In the rain, in the sun, in the mud, you're the person that just does it and keeps going, so that's why I'm pausing to tell you keep your head up and keep pushing. The win is in your sights and there ain't no stopping now. All right. In this step, you'll apply cross validation and thoroughly evaluate your model, but before we dive into the details, let's recap why this step is so important. In the previous step, you assessed how well your model performs on both the training and test data sets. However, there's a concern with this approach. Imagine you're preparing for a big exam and you only practice with one set of questions. You might become really good at answering those particular questions, but it doesn't guarantee you'll excel at the actual exam, which…
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Creating the linear regression model and model summary: Part 19 分钟 33 秒
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Creating the linear regression model and model summary: Part 27 分钟 16 秒
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Creating the linear regression model and model summary: Part 35 分钟 33 秒
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Dropping insignificant variables and re-creating the model7 分钟 57 秒
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Checking assumptions for linear regression3 分钟 18 秒
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Assumption 1: Checking for mean residuals2 分钟 47 秒
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Assumption 2: Checking homoscedasticity3 分钟 13 秒
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Assumption 3: Checking linearity2 分钟 12 秒
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Assumption 4: Checking normality of error terms3 分钟 24 秒
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Q-Q plot for checking the normality of error terms3 分钟 14 秒
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Model performance comparison on train and test data6 分钟 7 秒
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Applying cross-validation and evaluation4 分钟 40 秒
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Challenge: Model building48 秒
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Solution: Model building1 分钟 16 秒
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