So after yesterdays class, i started learning resampling tricks. I decided to dive into this whole resampling thing, especially this Cross Validation stuff.
- Resampling? It’s like when you take a test and then you keep redoing the same test over and over again. Or maybe you make up new tests based on the one you already did.
- Why Bother with Resampling? Well, imagine you’re trying to predict stuff with your fancy model, but you don’t have new data to test it on. That’s when resampling comes to the rescue. It helps you make up new data to see how your model does.
- Cross Validation’s Mission: Cross Validation is like your model’s bodyguard. It watches out for those sneaky mistakes caused by your model being too obsessed with the training data.
- Test Error vs. Training Error: Test Error is like the average oopsies your model makes when it meets new data. Training Error is more like the oopsies it makes when it’s practicing on its old pals.
- What’s Overfitting? Picture this: your model is trying to draw a line that’s so snug with certain dots that it forgets about the other dots. That’s overfitting, and it’s not great for making predictions.
- Cross-Validation 101: You split your data into two teams—the training team and the validation team. The training team is like your model’s personal trainer, getting it in shape. Then, the model tries to predict stuff for the validation team to see how well it learned.