semvec/pypi-packages huggingface dataset:
mcnets 2.0.7
Lightweight Machine Learning package with models that train using simple Monte Carlo-like methods.
Requires Python: >=3.7

# Monte-Carlo-Neural-Nets

## Overview
A very lightweight machine learning package.

Has models that use a unique and simple Monte Carlo approach to training. This method used is
very generalizable and can therefore be extended to a variety of models both known and new. 

The primary model, the `NeuralNetwork` class, is on par with other similar models such as the 
`MLPRegressor`/`MLPClassifier` featured in SciKit-Learn, but has more customizability.

The current list of models avaliable and some of their features includes:
- `NeuralNetwork`
    - Complete hidden layer size and activations customization
    - Supports externally defined activation functions
    - Allows customizing the input and output activations
    - Easy-access hyperparam ranges for Optuna (via .get_param_ranges_for_optuna)
- `SoupRegressor`
    - A unique combination of many various functions with trainable coefficients
    - Typically on-par with the NeuralNetwork; slightly more interpretable
    - Many hyperparams to adjust, with more to come

Some QoL functions and features included are:
- `TTSplit`: Included train-test splitter
- `cross_val`: Simple cross validation system
- Built-in scorer functions with support for external functions
- Ability to save and load models at any point (.save & load_model)
- Ability to copy a model via .copy

## GitHub and QuickStart
More explanations, examples, and technicals can be found on the GitHub page:
https://github.com/SciCapt/Monte-Carlo-Neural-Nets