As a developer, I'm working on regression and classification comparison of datasets and effectiveness.
Define of progress
[x] Produce both models
[x] Test different commotion of features.
Tasks completed
[x] Evaluate the output result compared to the first model, where too many features were implemented.
[x] Compare regression and classification output. The purpose of the two models is to identify different classes to support identity regression output. Not too much work, but it is effective in cross-validating the model.
[x] Random forest has shown the con and pros of ensemble decision trees. Therefore random specifically require a lot more to study as the upcoming sprint. I'm trying to complete modeling as soon as possible, but by this point, I'm not getting the optimal output I expected when I started this sprint. I need more study into how random forest can play effectively, not as a model for the whole knees, but specifically for certain predictions on specific bones, where x and y are two different joins and output the third dimension as the prediction of trees. Which I'm still working on it.
As a developer, I'm working on regression and classification comparison of datasets and effectiveness.
Define of progress
Tasks completed