- correlation matrix to be able to exclude some features to achieve better model performance
- how to evaluate classification model performance:
- ROC curve - True positive rate to false positive rate -> https://www.wikiwand.com/en/Receiver_operating_characteristic
- CAP curve - https://www.scribd.com/document/317871163/CAP-curve
- curse of dimensionality -> https://www.wikiwand.com/en/Curse_of_dimensionality
- dummy variable trap (with cathegorical data)
- in work we use python scikit-learn lib for ML .. this lib enable us to create so called pipelines - where you can specify N classification models each with M hyperparameter and each hyper parameter with K different values .. this pipeline uses cross validation and grid search algorithm to tell you the best classification model for you probelm