COLUMN EXPRyou can implement quite sophisticated feature selection logic (for example, in https://github.com/mldbai/mldb/blob/master/testing/MLDB-498-svd-apply-function.js#L101 we have a feature selection select as
select COLUMN EXPR (AS columnName() WHERE rowCount() > 100 ORDER BY rowCount() DESC, columnName() LIMIT 1000) from reddit_dataset, which selects the 1,000 most frequent (sparse) features that occur more than 1,000 times.
jsevalis a great escape hatch for when you need to do something that's not possible otherwise, and it is pretty fast, so that's another option. And you're certainly not bothering us, keep the questions coming!
mldb_data/plugins/autoload/<pluginname>directory, and it will be loaded by MLDB on startup.
/v1/functions/<classifier>/detailsalthough currently it's not possible to re-import that elsewhere.
SELECT [ classifier1(features), classifier2(features), classifier3(features) ]to return a 3-element vector with the three models.
ResourceError Traceback (most recent call last)
<ipython-input-71-bd023aae1a85> in <module>()
11 "algorithm": "bbdt",
---> 13 "modelFileUrl": "file:///mldb_data/factor_regressor.cls"
/usr/local/lib/python2.7/dist-packages/pymldb/init.pyc in inner(args, **kwargs)
16 result = add_repr_html_to_response(fn(args, **kwargs))
17 if result.status_code < 200 or result.status_code >= 400:
---> 18 raise ResourceError(result)
19 return result
20 return inner
ResourceError: '502 Bad Gateway' response to 'PUT http://localhost/v1/procedures/factor_regressor_model'
"error": "MLDB Unavailable",
"message": "MLDB is unable to respond either because it has not finished booting or because it has crashed and has not finished rebooting. Recent log messages are available via HTTP at /logs/mldb"