There is an increasing need for **transparency** and **fairness** in Machine Learning (ML) models predictions. Consider for example a banker who has to explain to a client why his/her loan application is rejected, or a health professional who must explain what constitutes his/her diagnosis. Some ML models are indeed very accurate, but are considered hard to explain, relatively to popular linear models.

**Source of figure**: James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.

We do not want to sacrifice this high accuracy to explainability. Hence: **ML explainability**. There are a lot of ML explainability tools out there, *in the wild* for that purpose (don’t take my word for it).

The `teller`

is a **model-agnostic tool for ML explainability** - agnostic, as long as this model possesses methods `fit`

and `predict`

. The `teller`

’s philosophy is to rely on Taylor series to explain ML models predictions: a little increase in model’s explanatory variables + a little decrease, and we can obtain approximate sensitivities of its predictions to changes in these explanatory variables.

## Installation

Currently from Github, for the development version:

```
pip install git+https://github.com/Techtonique/teller.git
```

## Package description

This notebook will give you a good introduction:

thierrymoudiki_011119_boston_housing.ipynb

Two models are used in the notebook: a **linear model** and a Random Forest (here, the *black-box* model). The most straightforward way to illustrate the `teller`

is to use a linear model. In this case, the effects of model covariates on the response can be directly related to the linear model’s coefficients. Also, note that if there a lot of variables in your model, the `teller`

’s explainer can be created with option `n_jobs=-1`

(for parallel execution).

Contributions/remarks are welcome as usual, you can submit a pull request on Github.

**Note:** I am currently looking for a *gig*. You can hire me on Malt or send me an email: **thierry dot moudiki at pm dot me**. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!