Project description

overfit

ML training code generator. Not ready for use.

Examples

from overfit.unsupervised import UnsupervisedPipeline
fit = UnsupervisedPipeline(data = data,
                         target = 'target_name',
                         type = 'kmeans',
                         nfolds = 10
                         )
# fit.get_code(), print
fit.code_gen() # run

Project details

These details have not been verified by PyPI

Project links

GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Release history Release notifications | RSS feed

This version

0.0.1

Feb 23, 2024

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

overfit-0.0.1.tar.gz (2.3 kB view hashes)

Uploaded Feb 23, 2024 Source

Built Distribution

overfit-0.0.1-py3-none-any.whl (2.3 kB view hashes)

Uploaded Feb 23, 2024 Python 3

Hashes for overfit-0.0.1.tar.gz

Hashes for overfit-0.0.1.tar.gz
Algorithm	Hash digest
SHA256	`d545e735f97ffdcc2fa94fbf00b255bc073ed9b80e8c8f3063e4747d9fae5c01`
MD5	`17fb9cd6dd883f071c60cf745a427764`
BLAKE2b-256	`8e580f7882034a2330e37314b27d95b08054a90940955e9eaef741f840724a72`

Hashes for overfit-0.0.1-py3-none-any.whl

Hashes for overfit-0.0.1-py3-none-any.whl
Algorithm	Hash digest
SHA256	`27957fd45fdbcf53d3da30fd9147006bad257c0bac886c9abe6be43d07f68529`
MD5	`30013af71584d7f0141929627c3b5263`
BLAKE2b-256	`8bdd46c8372df02afbe4ac968b6755fc8fc5911ba1cf352b1c88c4fc5cdd1e3b`