semvec/pypi-packages huggingface dataset:
machineLearningModel 1.0.0
Machine Learning models make simple
Keywords: python,classification,regression,machine learning models,hyper parameter turning

# Project Title

Machine Learning Models Made Simple

## Guide

### Classfication model

    Classifier->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]
                [function]
                model_training
                model_accuracy(y_test_f,y_pred_f,model_name,model_obj)

                logistic_regression
                kneighbors_classifier
                gaussian_nb
                bagging_classifier
                extra_trees_classifier
                ridge_classifier
                sgd_classifier
                random_forest_classifier
                xgb_classifier
                ada_boost_classifier
                bernoulli_nb
                gradient_boosting_classifier
                decision_tree_classifier
                svc

                [Hyperparameter tuning]

                hyperparameter_tuning

                logistic_hyperparameter
                knn_hyperparameter
                gaussian_nb_hyperparameter
                bernoulli_nb_hyperparameter
                ridge_hyperparameter
                adaboost_hyperparameter
                gradient_boosting_hyperparameter
                svc_hyperparameter
                decision_tree_hyperparameter

### Regression model

    Regressor->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]
                [function]
                model_training
                model_accuracy(y_test_f,y_pred_f,model_name,model_obj)

                linear_regression
                ridge_regression
                lasso_regression
                elastic_net_regression
                sgd_regression
                random_forest_regression
                kneighbors_regression
                decision_tree_regression
                ada_boost_regression
                xgboost_regression
                gradient_boosting_regression
                theilsen_regression
                ransac_regression
                lasso_lars_regression
                lars_regression
                orthogonal_regression
                huber_regression
                svr
                passive_aggressive_regression
                ard_regression
                bayesian_ridge_regression
                bagging_regression
                extra_trees_regression
pydis 0.8.2
Python wrappers for StrictRedis
Requires Python: >=3.6

# pydis

Python wrappers for StrictRedis inspired by https://blog.jverkamp.com/2015/07/16/automagically-storing-python-objects-in-redis/

<a href='https://coveralls.io/github/masayang/py_redis?branch=master'><img src='https://coveralls.io/repos/github/masayang/py_redis/badge.svg?branch=master' alt='Coverage Status' /></a>

## Installation

pip install -U pydis

## Configuration

See redis_settings.py for detail.

## Usage

See tests/sample.py

## test
python -m pytest --cov-report term-missing --cov=pydis
chlore 0.0.1
Front-end to write automated tests for Etna projects

# quixote

Front-end library to script automated jobs, with an emphasis on automatic school work correction.

## Documentation

The documentation is hosted at [etna-alternance.github.io/quixote](https://etna-alternance.github.io/quixote).

To build the documentation locally, run:

``` bash
pip install -r docs/requirements.txt
make -C docs html
```

## Tests

Tests can be run by installing PyTest and then calling it like below:

```
$ pip3 install pytest
$ pytest tests/*/*.py
```
machineLearningModelPKG 1.0.0
Machine Learning models make simple
Keywords: python,classification,regression,machine learning models,hyper parameter turning

# Project Title

Machine Learning Models Made Simple

## Guide

### Classfication model

    Classifier->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]
                [function]
                model_training
                model_accuracy(y_test_f,y_pred_f,model_name,model_obj)

                logistic_regression
                kneighbors_classifier
                gaussian_nb
                bagging_classifier
                extra_trees_classifier
                ridge_classifier
                sgd_classifier
                random_forest_classifier
                xgb_classifier
                ada_boost_classifier
                bernoulli_nb
                gradient_boosting_classifier
                decision_tree_classifier
                svc

                [Hyperparameter tuning]

                hyperparameter_tuning

                logistic_hyperparameter
                knn_hyperparameter
                gaussian_nb_hyperparameter
                bernoulli_nb_hyperparameter
                ridge_hyperparameter
                adaboost_hyperparameter
                gradient_boosting_hyperparameter
                svc_hyperparameter
                decision_tree_hyperparameter

### Regression model

    Regressor->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]
                [function]
                model_training
                model_accuracy(y_test_f,y_pred_f,model_name,model_obj)

                linear_regression
                ridge_regression
                lasso_regression
                elastic_net_regression
                sgd_regression
                random_forest_regression
                kneighbors_regression
                decision_tree_regression
                ada_boost_regression
                xgboost_regression
                gradient_boosting_regression
                theilsen_regression
                ransac_regression
                lasso_lars_regression
                lars_regression
                orthogonal_regression
                huber_regression
                svr
                passive_aggressive_regression
                ard_regression
                bayesian_ridge_regression
                bagging_regression
                extra_trees_regression