Underfitting
Underfitting is a common problem in machine learning where a model is too simplistic to adequately represent the underlying data patterns.
Underfitting occurs when a machine learning model is overly simplistic and fails to capture the intricate patterns within our data. This can lead to poor predictive performance due to a high bias, as the model oversimplifies relationships that are actually more complex.
In today’s class, we talked about some data related to crabs shedding their old shells. This shedding process is called molting, and it helps crabs grow. Our goal was to figure out how big a crab was before it molted based on how big it was after molting.
At first, we used a math method called a linear model, and it gave us a number called R^2, which was 0.98. We also looked at some numbers that describe the data. The numbers showed that the data was a bit unusual. When we made graphs of the data, they looked similar, just with a little shift in the middle. To check if they were really similar, we did a test called a T-test. This test helps us see if there’s a big difference between the two sets of data.
The T-test told us that there is a significant difference between the sizes before and after molting. We used a method called ‘Monte-Carlo’ to estimate how big this difference is.