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1. Project Overview & Quickstart (mljar/LICENSE.txt) Copy Chapter

LICENSE.txt

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2. Official Technical Reference & Guides (mljar/docs) Copy Chapter

File: README.md Copy Chapter

Moved to https://github.com/mljar/mljar-supervised

Docs

Docs for mljar-supervised available at https://supervised.mljar.com

Update `mljar-supervised` API Copy Section

pip install -U -r requirements.txt

Development Copy Section

mkdocs serve

Deploy Copy Section

mkdocs build
aws s3 sync site/ s3://supervised.mljar.com

File: docs/index.md Copy Chapter

Get started

The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist :sunglasses:. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model :trophy:. It is no black-box as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).

The mljar-supervised will help you with:

• explaining and understanding your data,
• trying many different machine learning models,
• creating Markdown reports from analysis with details about all models,
• saving, re-running and loading the analysis and ML models.

What's good in it? Copy Chapter

• mljar-supervised creates markdown reports from AutoML training full of ML details and charts.
• It can compute the 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.
• This package is training simple Decision Trees with max_depth <= 5, so you can easily visualize them with amazing dtreeviz to better understand your data.
• The 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.
• It is using a many algorithms: Baseline, Linear, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, Neural Networks, and Nearest Neighbors.
• It can do features preprocessing, like: missing values imputation and converting categoricals. What is more, it can also handle target values preprocessing (You won't believe how often it is needed!). For example, converting categorical target into numeric.
• It can tune hyper-parameters with not-so-random-search algorithm (random-search over defined set of values) and hill climbing to fine-tune final models.
• It can compute Ensemble based on greedy algorithm from Caruana paper.
• It can stack models to build level 2 ensemble (available in Compete mode or after setting stack_models parameter).
• It cares about explainability of models: for every algorithm, the feature importance is computed based on permutation. Additionally, for every algorithm the SHAP explanations are computed: feature importance, dependence plots, and decision plots (explanations can be switched off with explain_level parameter).

Installation Copy Chapter

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

Running in docker with Jupyter notebook:

FROM python:3.7-slim-buster
RUN apt-get update && apt-get -y update
RUN apt-get install -y build-essential python3-pip python3-dev
RUN pip3 -q install pip --upgrade
RUN pip3 install mljar-supervised jupyter
CMD ["jupyter", "notebook", "--port=8888", "--no-browser", "--ip=0.0.0.0", "--allow-root"]

Basic usage Copy Chapter

import pandas as pd
from sklearn.model_selection import train_test_split
from supervised.automl import AutoML

df = pd.read_csv(
    "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv",
    skipinitialspace=True,
)
X_train, X_test, y_train, y_test = train_test_split(
    df[df.columns[:-1]], df["income"], test_size=0.25
)

automl = AutoML()
automl.fit(X_train, y_train)

predictions = automl.predict(X_test)