A lightweight automl library.
Project description
A lightweight automl library.
Installation
pip install -U cutoml
Usage Example
For classification,
from cutoml.cutoml import CutoClassifier
from sklearn.model_selection import train_test_split
from sklearn import datasets
dataset = datasets.load_digits()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
dataset.target,
test_size=0.2)
ctc = CutoClassifier(k_folds=3, n_jobs=-1, verbose=1)
ctc.fit(X=X_train, y=y_train)
For regression,
from cutoml.cutoml import CutoRegressor
from sklearn.model_selection import train_test_split
from sklearn import datasets
dataset = datasets.load_boston()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
dataset.target,
test_size=0.2)
ctr = CutoRegressor(k_folds=3, n_jobs=-1, verbose=1)
ctr.fit(X=X_train, y=y_train)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
cutoml-0.0.9.post11.tar.gz
(6.9 kB
view details)
File details
Details for the file cutoml-0.0.9.post11.tar.gz.
File metadata
- Download URL: cutoml-0.0.9.post11.tar.gz
- Upload date:
- Size: 6.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a72ef4a743e4b84dc6c4e8272bbd9bd1a260cfbb973a42e9a87a7e7d9cc6224e
|
|
| MD5 |
0c145bb9011906609c171f10f1568a1c
|
|
| BLAKE2b-256 |
86652995b1a0ca7f3a9dfcb63c101918d5d1afc2ed3272999c09fd546552fe03
|