When using sklearn, I've seen a lot of folks just pickle the model and use that as the interchange format. I like the human-readable interchange format you are using better. I assume you just rolled your own. Why not something like PMML?
{
"intercept": 1.0,
"features": {
"feature_1": {
"coefficient": 1.0,
"range": [0.1, 10.0],
"mean_feature_score": 1.0,
"imputation_value": 1.0
},
{
....
}
}
}Note that it's very tied down to our use case right now: only compatible with Logistic Regression, and currently it assumes fixed hyperparameters (will change this in future though), assumes a production pipeline of min-max scaling, imputation, then classification.