Skip to main content

# Auto Machine Learning Models

## Overview auto-ml-models is a Python package to find best machine learning model built Based on scikit-learn, catboost and xgboost.

## Features ### This project include 9 models: * KNN * Linear * SVM * DecisionTree * RandomForest * GBoost * AdaBoost * XGBoost * CatBoost

### This project include 6 scores: * Accuracy * Precision * Recall * F1 * MAE * MSE

## Installation

Install the package

` pip install auto-ml-models `

## Usage

` pip install auto-ml-models `

Metadata

Release files for auto-ml-models 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for auto-ml-models 0.1.0
File Size Uploaded
auto_ml_models-0.1.0.tar.gz 3.5 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for auto-ml-models 0.1.0
File Interpreter ABI Platform
auto_ml_models-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 8.1 kB

Release files / auto_ml_models-0.1.0.tar.gz

Download URL auto_ml_models-0.1.0.tar.gz
Size 3.5 kB
Tags Source
SHA-256 checksum
How to use checksums
6085f12065d63f0b3a9908ca0e7f58c420fa22077e77941ee8f92b9fa48c947a
BLAKE2b-256 checksum
How to use checksums
270ab4f5ac1e0a787a60e6aef57dc132c2ca55ac702e64a51ae296d82f295f2d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.9.19

Release files / auto_ml_models-0.1.0-py3-none-any.whl

Download URL auto_ml_models-0.1.0-py3-none-any.whl
Size 4.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d3162c6693097dcfe3a407184cc7eefab0cf2dd2862e10655d4d5dd4c0c73f40
BLAKE2b-256 checksum
How to use checksums
221d83b6dbd61ee6ee98b7c0520e943c8221ceb2000b6afbe1c31cf8e6235e0a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.9.19

Release history Release notifications | RSS feed

This release

0.1.0 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page