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Evaluating regression model predictions

Evaluating regression model predictions - Python Tutorial

From the course: Supervised Learning Essential Training

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Evaluating regression model predictions

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- [Instructor] Arguably the most important part of building machine learning models is properly evaluating our models. By running our logistic regression and printing the summary we can see how well our regression model performed. Starting here by importing our libraries, getting our data, there we go, and then running our regression model. Here, let's dig into some of these metrics. First, the R squared signifies the percentage variation that's explained by the independent variables. Here, our 81% variation in the G3 or RY is explained by G2. Our probability or F statistic, this basically tells us the overall significance of our regression. Under that, we see both AIC and BIC. So let's dig into it. AIC stands for Akaike Information Criterion and is used for model selection. It penalize the errors in case a new variable is added to the regression equation. Whereas BIC stands for Bayesian Information Criterion…

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