Hi all,
I have a work project where I have 10000 datasets, each dataset consists of Training, Validating, Testing data.
The data-mining/machine learning problem is a binary classification problem. And of course there are a bunch of models to compare and select, e.g. Logistic Regression, xgboost, SVC, Deep Learning etc.
I have to compare classification performance metrics across all datasets and all models.
So the work-flow is:
for model in models:
for data in datasets:
train the model, and collect testing metrics.
collect metric numbers for all data and calculate their mean.
compare the means across models and make a table of the metrics for all model.
Is there an easy-to-use framework already present for the above model-compare/selection process?
Since these are common models, I would imagine there are already out-of-box solutions for such model comparison.
Could anybody give me some pointers?
Thanks a lot!