I'm applying ML for classification task in Python using sklearn/pandas. I'm going to try various things to get the best results, and I wonder how do I effectively store and analyze all the parameters and results of the classification? Parameters include:
- Number of training examples (which can be extended as I get more labeled data).
- Set of features.
- Classification algorithm.
- Algorithm hyperparameters.
- Precision/recall for each of the classes.
- Overall precision/recall.
- Support for each class, etc.
Of course, I can manually copy the parameters and results to an Excel spreadsheet every time, but it's not an optimal solution. Are there any Python libraries (or modules of sklearn/pandas) which allow to easily store and display the parameters and results for later analysis? How do you solve this task?