mljar-supervised is an Automated Machine Learning python package. It can train ML models for:
mljar-supervisedcreates markdown reports from AutoML training. The example of AutoML leaderboard summary:
The example for
Decision Tree summary:
The example for
Baselinefor 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
Baselineis computed based on prior class distribution for classification, and simple mean for regression.
max_depth <= 5, so you can easily visualize them with amazing dtreeviz to better understand your data.
mljar-supervisedis 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.
Neural Networkswill be added soon).
not-so-random-searchalgorithm (random-search over defined set of values) and hill climbing to fine-tune final models.
There is a simple interface available with
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
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.