Hello @shaweln and welcome to the MindsDB forum. Actually, those are great questions.
At this moment we have two ways to make a batch prediction:
- First is using ‘select_data_query’ in ‘where’. Here you will need to specify select statement as text, for example:
select rental_price from mindsdb.rental_price_predictor where select_data_query = ‘select sqft, number_of_rooms, location from rental_price_data order by publish_date limit 5’
If you use ‘select_data_query’ in ‘where’, then there should be no other conditions. The requirements to query in ‘select_data_query’ is:
- it must be a valid SQL statement
- it must return columns with names the same as predictor fields.
- Second is using existing ‘external_datasource’ in where. Like ‘select_data_query’, it should be the only statement in ‘where’. Here you can specify any existing MindsDB datasource. So you can do the following:
- prepare csv file with data for prediction e.g:
- add it as datasource using python module or Scout.
- make query:
select rental_price from mindsdb.rental_price_predictor where external_datasource = ‘ds_name’;
For the second question, we don’t have a specific similarity score but you can check MindsDB Scout. After starting MindsDB, Scout should be accessible on http://localhost:47334/static/index.html. There we have explainability features like data analysis, data quality, prediction results like model accuracies, what’s relevant to the model, when can you trust it etc.