This repository contains a solution to the multiclass classification problem on the Iris dataset using the CatBoostClassifier model.

How to use

Basic usage

• python3 train.py for model training
• python3 infer.py to get predictions on the test dataset, dataset with obtained predictions will be stored in data > predicts

Advanced usage

Modifying training hyperparameters

Go to file train.yaml located in the "configs" directory and modify model > optimizer_parameters section

Modifying metrics

Go to file train.yaml located in the "configs" directory and modify model > custom metrics section (please refer to CatBoost documentation to get available metrics)

Implementation details

Data

• Datasets are stored in dvc using Google Drive as backend
• All files created during training / inferring are also saved to dvc

Logging

• Training parameters and metrics are logged using MLFlow
• Start and end of training / inferring steps are logged to console