mljar-supervised
is an Automated Machine Learning python package. It can train ML models for:
mljar-supervised
creates markdown reports from AutoML training. The example of AutoML leaderboard summary:The example for Decision Tree
summary:
The example for LightGBM
summary:
Baseline
for your data. So you will know if you need Machine Learning or not! You will know how good are your ML models comparing to the Baseline
. The Baseline
is computed based on prior class distribution for classification, and simple mean for regression.Decision Trees
with max_depth <= 5
, so you can easily visualize them with amazing dtreeviz to better understand your data.mljar-supervised
is using simple linear regression and include its coefficients in the summary report, so you can check which features are used the most in the linear model.Random Forest
, Extra Trees
, LightGBM
, Xgboost
, CatBoost
(Neural Networks
will be added soon).not-so-random-search
algorithm (random-search over defined set of values) and hill climbing to fine-tune final models.explain_level
parameter).There is a simple interface available with fit
and predict
methods.
import pandas as pd
from supervised.automl import AutoML
df = pd.read_csv("https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv", skipinitialspace=True)
X = df[df.columns[:-1]]
y = df["income"]
automl = AutoML(results_path="directory_with_reports")
automl.fit(X, y)
predictions = automl.predict(X)
For details please check AutoML API Docs.
From PyPi repository:
pip install mljar-supervised
From source code:
git clone https://github.com/mljar/mljar-supervised.git
cd mljar-supervised
python setup.py install
Installation for development
git clone https://github.com/mljar/mljar-supervised.git
virtualenv venv --python=python3.6
source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements_dev.txt
The mljar-supervised
is an open-source project created by MLJAR. We care about ease of use in the Machine Learning.
The mljar.com provides a beautiful and simple user interface for building machine learning models.