Human Learning

Machine Learning models should play by the rules, literally.

Project Goal

Back in the old days, it was common to write rule-based systems. Systems that do;

Nowadays, it's much more fashionable to use machine learning instead. Something like;

We started wondering if we might have lost something in this transition. Sure, machine learning covers a lot of ground but it is also capable of making bad decision. We need to remain careful about hype because and we shouldn't forget that many classification problems can be handled by natural intelligence too.

This package contains scikit-learn compatible tools that should make it easier to construct and benchmark rule based systems that are designed by humans. You can also use it in combination with ML models.

Installation

You can install this tool via pip.

python -m pip install human-learn

Documentation

Detailed documentation of this tool can be found here.

A free video course can be found on calmcode.io.

Features

This library hosts a couple of models that you can play with.

Interactive Drawings

This tool allows you to draw over your datasets. These drawings can later be converted to models or to preprocessing tools.

Classification Models

FunctionClassifier

This allows you to define a function that can make classification predictions. It's constructed in such a way that you can use the arguments of the function as a parameter that you can benchmark in a grid-search.

InteractiveClassifier

This allows you to draw decision boundaries in interactive charts to create a model. You can create charts interactively in the notebook and export it as a scikit-learn compatible model.

Regression Models

FunctionRegressor

This allows you to define a function that can make regression predictions. It's constructed in such a way that you can use the arguments of the function as a parameter that you can benchmark in a grid-search.

Outlier Detection Models

FunctionOutlierDetector

This allows you to define a function that can declare outliers. It's constructed in such a way that you can use the arguments of the function as a parameter that you can benchmark in a grid-search.

InteractiveOutlierDetector

This allows you to draw decision boundaries in interactive charts to create a model. If a point falls outside of these boundaries we might be able to declare it an outlier. There's a threshold parameter for how strict you might want to be.

Preprocessing Models

PipeTransformer

This allows you to define a function that can handle preprocessing. It's constructed in such a way that you can use the arguments of the function as a parameter that you can benchmark in a grid-search. This is especially powerful in combination with the pandas .pipe method. If you're unfamiliar with this amazing feature, you may appreciate this tutorial.

InteractivePreprocessor

This allows you to draw features that you'd like to add to your dataset or your machine learning pipeline. You can use it via tfm.fit(df).transform(df) and df.pipe(tfm).

Datasets

Titanic

This library hosts the popular titanic survivor dataset for demo purposes. The goal of this dataset is to predict who might have survived the titanic disaster.

Fish

The fish market dataset is also hosted in this library. The goal of this dataset is to predict the weight of fish. However, it can also be turned into a classification problem by predicting the species.