semvec/pypi-packages huggingface dataset:
verifiably-wsock-secrets-provider 0.0.7
Secrets provider that communicates with trusted execution environments over web sockets.
Requires Python: >=3.8
Keywords: python,web-socket,credentials

# Web socket secrets provider

This library enables an easy setup for a secret provider trough wsock. This secrets provider was developed to be used with a [vFunction](https://developer.verifiably.com/docs/vfunctions/what-is-a-vfunction).

## Installation
To install this library run:

```
pip install verifiably_wsock_secrets_provider
```

## Setup
To create the server, just provide your secrets and start the communication trough wsock.

```python
from verifiably_wsock_secrets_provider import credentials_provider

credentials = {
    "account1":{
        "accountId": "1234",
        "token": "0000"
    }
}


expected_pcrs = {
    "0":"0000"
}

secretProvider = credentials_provider.SecretsProvider(credentials, expected_pcrs)

secretProvider.start()
```
verifiably-checker 0.0.3
Library to verify the results sent by a vFunction.
Requires Python: >=3.8
Keywords: python,security,verification,attestation

# Verifiably Checker

This library offers an easy way to verify the results sent by a vfunction. For more details about the process visit the [Attestation Documentation](https://developer.verifiably.com/docs/vfunctions/attestation).

## Installation
To install this library run:

```
pip install verifiably_checker
```
model-selector 1.0.2
This package will help you select the regression model or classification model for your dataset. Provide the file path to your dataset and watch this package do the rest of the work for you.


# Model-Selection
There are a lot of predictive modeling algorithms in machine learning (ML) in engineering applications. Predictive modeling is the problem of developing a model using historical data to predict new data where we do not have the answer. Predictive modeling can be described as the mathematical problem of approximating a mapping function (f) from input variables (X) to output variables (y). This is called the problem of function approximation. Generally, we can divide all function approximation tasks into classification and regression tasks. 
Regression models include Single and Multiple Linear Regression, Decision Tree, Polynomial, Random Forest, and Support Vector. Classification models include Decision Tree, K-Nearest Neighbors, Kernel SVM, Logistic Regression, NaÃ¯ve Bayes, Random Forest, and Support Vector Machine.
One of the most frequently asked questions in the data science community seeks to determine which regression or classification model is best suited to be used on different datasets. This project aims to build a python package that will help you evaluate your regression models to select the best model for your dataset quickly and efficiently. Provide the file path to your dataset with some optional parameters and watch this package do the rest of the work for you.
adhocboost 0.0.6
A model specialized for imbalanced class learning.
Requires Python: >=3.6

# AdHocBoost
[![PyPI version](https://badge.fury.io/py/adhocboost.svg)](https://github.com/deliveryhero/adhocboost)
[![License](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/deliveryhero/adhocboost)

Welcome to AdHocBoost--a model that is specialized for classification in a severely imbalanced-class scenario.

## About
Many data science problems have severely imbalanced classes (e.g. predicting fraudulent transactions, predicting
order-cancellations in food-delivery, predicting if a day in Berlin will be sunny). In these situations, predicting the
positive class is hard! This module aims to alleviate some of that.

The `AdHocBoost` model works by creating `n` sequential models. The first `n-1` models can most aptly be thought of
as dataset filtering models, i.e. each one does a good job at classifying rows as "definitely _not_ the positive class"
versus "maybe the positive class". The `nth` model only works on this filtered "maybe positive" data.

Like this, the class imbalance is alleviated at each filter-step, such that by the time the dataset is filtered for
final classification by the `nth` model, the classes are considerably more balanced.

## Run Instructions
Installation is with `pip install adhocboost`. Beyond that, `AdHocBoost` conforms to a sklearn-like API: to use
it, you simply instantiate it, and then use `.fit()`, `.predict()`, and `.predict_proba()` as you see... fit ;)