Skip to main content

Traditional machine learning model development is resource-intensive, requiring significant domain/statistical knowledge and time to produce and compare dozens of models. With automated machine learning, the time it takes to get production-ready ML models with great ease and efficiency highly accelerates. However, the Automated Machine Learning does not yet provide much in terms of data preparation and feature engineering. The AutoBrewML tries to solve this problem at scale as well as simplifies the overall process for the user. It leverages the Automated ML coupled with components like Data Profiler, Data Sampler, Data Cleanser, Anomaly Detector which ensures quality data as a critical pre-step for building the ML model. With AutoBrewML the time it takes to get production-ready ML models with great ease and efficiency highly accelerates

Modules

  • Acquisition_DataTypeConversion : Data Acquisition & Transformation

  • BrewDataProfiler : Exploratory Data Analysis

  • BrewDataSampling : Data Sampling

    • Random Sampling

    • Stratified Sampling

    • Systematic Sampling

    • Cluster OverSampling (with SMOTE)

  • BrewDataCleanser : Data Cleansing

  • BrewAnomalyDetection : Anomaly Detection

  • BrewTrainTestSplit : Train-Test Split the data in a given ratio

  • BrewFeatureSelection : Selection of Most Important Features before Modelling

  • BrewAutoML_Classifier : Auto trigger and choose ML Model for Classification

  • BrewAutoML_Regressor : Auto trigger and choose ML Model for Regression

  • BrewAutoML_TimeSeries : Auto trigger and choose ML Model for Time series Forecasting

  • Responsible AI Guidelines

    • BrewFairnessEvaluator : Evaluate the Fairness of a Model with respect to metrics across various cohorts of a sensitive feature

    • BrewDisparityMitigation : Mitigate the Bias observed in above Evaluator

Prerequisites: - Python version >=3.8.0

Metadata

Release files for AutoBrewML 0.45

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

Built distribution (wheel)

Table of built distributions (wheels) for AutoBrewML 0.45
File Interpreter ABI Platform
AutoBrewML-0.45-py3-none-any.whl Python 3 none any Details

Release files / AutoBrewML-0.45-py3-none-any.whl

Download URL AutoBrewML-0.45-py3-none-any.whl
Size 17.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
1727b438bd6c178b3b3e14fb878050dc5de97151215ecf3e74f479c42a13e14a
BLAKE2b-256 checksum
How to use checksums
899f6e78a9df512e0a2c4269d4815857bb88456b389885b7fd836ef0ed934f2c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.13

Release history Release notifications | RSS feed

This release

0.45 This release

1 release file

0.44

1 release file

0.43

1 release file

0.42

1 release file

0.41

1 release file

0.40

1 release file

0.39

1 release file

0.38

1 release file

0.37

1 release file

0.36

1 release file

0.35

1 release file

0.34

1 release file

0.33

1 release file

0.32

1 release file

0.31

1 release file

0.30

1 release file

0.29

1 release file

0.28

1 release file

0.27

1 release file

0.26

1 release file

0.25

1 release file

0.24

1 release file

0.23

1 release file

0.22

1 release file

0.21

1 release file

0.20

1 release file

0.18

1 release file

0.17

1 release file

0.16

1 release file

0.15

1 release file

0.14

1 release file

0.13

1 release file

0.12

1 release file

0.11

1 release file

0.10

1 release file

0.9

1 release file

0.8

1 release file

0.7

1 release file

0.6

1 release file

0.5

1 release file

0.4

1 release file

0.3

1 release file

0.2

2 release files

0.1

1 release file

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