Iris classification

This project is a template implementation of a classification problem.
It can be quickly adapted to fit a regression task.

3 implementations are proposed:

1. Neural network implemented with Keras
2. xgboost implmented with xgboost
3. Decision tree implemented with sklearn

There used to be a 4th one, Neural network implemented with TensorFlow, however since TF 2.0 it got all messed up...

• 1 Installation
• 2 Data
• 3 Training
• 4 Trained models
• 5 Evaluation
• 6 Results

Installation

The code was tested with Python 3.6.

Setting up a virtual environment (optional)

$ python3 -m pip install --user --upgrade virtualenv
$ python3 -m virtualenv env

Sourcing the environment:

$ source ./env/bin/activate

Installing the python packages

pip3 install -r requirements.txt 

Data

In the data folder.

Training

For each model in models, the training is performed by running train_model.py.

Trained models

For each model in models, a trained model is saved under trained_model/model_name.save.

Evaluation

An evaluation is performed on a test set at the end of each training. It is also possible to use the trained model to make prediction through the make_pred.py.

Results

Any model performs extremely well on the iris dataset ;-)