Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Accuracy

Precision/Recall

F1

Matthrews Correlation Coefficient (MCC)

ROC/AUC

Mean Absolute Error (MAE)

Mean Squared Error (MSE)

Root-mean-square Error (RMSE)

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

References

https://cloud.google.com/tpu/docs/tutorials/resnet-alpha-py

https://cloud.google.com/tpu/docs/tutorials

https://towardsdatascience.com/running-pytorch-on-tpu-a-bag-of-tricks-b6d0130bddd4

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Regularization add a penalty term to the loss function in machine learning models. The type of regularizatin depends on the type of penalty used (not the type of the objective function …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

L-BFGS converges faster and with better solutions on small datasets. However, ADAM is very robust for relatively large datasets. It usually converges quickly and gives pretty good performance. SGD with momentum …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

1. It is suggested that you use the sklearn wrapper classes XGBClassifier and XGBRegressor so that you can fully leverage other tools of the sklearn package.

2. There are 2 types of boosters …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

XGBoost

https://xgboost.ai/

XGBoost Documentation

Speedup XGBoost

https://machinelearningmastery.com/best-tune-multithreading-support-xgboost-python/

https://medium.com/data-design/xgboost-gpu-performance-on-low-end-gpu-vs-high-end-cpu-a7bc5fcd425b

xgboost GPU is fast. Very fast. As long as it fits in RAM and …