课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
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Solution: Model building - SQL教程
课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
Solution: Model building
(bright music) - [Presenter] So here's the solution to our challenge. When evaluating your car price prediction model, it's crucial to check for the mean residuals. These residuals represent the differences between your model's predicted car prices, and the actual prices. Think of them as the remaining pieces of a puzzle after you've tried to fit all the parts together. If the mean of the residuals is very close to zero, it's a positive sign. It means that on average, your predictions are hitting the mark. They're very close to the actual car prices. It's like consistently getting your darts right in the center of the bullseye when playing a game of darts. This indicates that your car price prediction model is accurate and trustworthy. In simple terms, checking mean residuals ensures that your model doesn't consistently over or underestimate car prices, making it a reliable tool for predicting used car prices.
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内容
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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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