Scikit learn library models to account for data and concept drift.

Overview

liquid_scikit_learn

Scikit learn library models to account for data and concept drift.

This python library focuses on solving data drift and concept drift in the industry to minimize retraining of the models regularly. After inspired about the capabilities of neurons in octopus tentacles, which they interact and adapt directly with the environment without their central nervous system. I designed the weights for these models in the similar way where they train on input and experience. Instead of calculating weights based on minimizing the loss function, derivatives of weights are calculated. ( Hasani Chen). This library also provides model expiration details at a feature level. This could help in finding the features that model has hard time adjusting.

image This library adapts concepts from Nueral ODE for scikit-learn. The models in this librabry calculate the derivatives of weights instead of weights as in standard scikit-learn librabry.

There are two training phases, the first one is a standard scikit learn model that provides predictions and weights for each feature. Typically, in standard ML models, training data is sent in batches and inferences can be done real time and in batch. In this scenario for the second training phase, input data is sent in semi batches and model adapts with changing data drift and concept drift with time. The second training phase along with changing weights it provides decay rate for each weight, contribution from data drift and concept drift and model failure parameters.

For example, suppose we train three months of data in the first training phase for the model to understand patterns with its provided inputs and outputs. In the second phase of training, we send weekly batches of inputs and outputs to make the model to adapt to changes in data and output that typically changes with customer behavior. I will make efforts to extend this library for unsupervised learning also. Currently liquid logistic regression is available with limited parameter optimization.

To use this librabry for now, git clone the librarby and give path to the librarby.

To use standard logistic regression

from liquid_scikit_learn.liquid_logistic_regression import logistic_regression

To use liquid logistic regression

from liquid_scikit_learn.liquid_logistic_regression import liquid_logistic_regression

To get model expiration details at a feature level

from liquid_scikit_learn.liquid_logistic_regression import model_failure
Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Trading Tesla with Machine Learning and Sentiment Analysis An interactive program to train a Random Forest Classifier to predict Tesla daily prices us

Renato Votto 31 Nov 17, 2022
Python module for performing linear regression for data with measurement errors and intrinsic scatter

Linear regression for data with measurement errors and intrinsic scatter (BCES) Python module for performing robust linear regression on (X,Y) data po

Rodrigo Nemmen 56 Sep 27, 2022
Getting Profit and Loss Make Easy From Binance

Getting Profit and Loss Make Easy From Binance I have been in Binance Automated Trading for some time and have generated a lot of transaction records,

17 Dec 21, 2022
MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees.

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. Th

Swiggy 66 Dec 06, 2022
A webpage that utilizes machine learning to extract sentiments from tweets.

Tweets_Classification_Webpage The goal of this project is to be able to predict what rating customers on social media platforms would give to products

Ayaz Nakhuda 1 Dec 30, 2021
Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
Machine Learning Course with Python:

A Machine Learning Course with Python Table of Contents Download Free Deep Learning Resource Guide Slack Group Introduction Motivation Machine Learnin

Instill AI 6.9k Jan 03, 2023
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy m

Robin 55 Dec 27, 2022
PySpark ML Bank Churn Prediction

PySpark-Bank-Churn Surname: corresponds to the record (row) number and has no effect on the output. CreditScore: contains random values and has no eff

kemalgunay 2 Nov 11, 2021
Reproducibility and Replicability of Web Measurement Studies

Reproducibility and Replicability of Web Measurement Studies This repository holds additional material to the paper "Reproducibility and Replicability

6 Dec 31, 2022
CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects

CML with cloud compute This repository contains a sample project using CML with Terraform (via the cml-runner function) to launch an AWS EC2 instance

Iterative 19 Oct 03, 2022
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Microsoft 14.5k Jan 07, 2023
Machine Learning Algorithms ( Desion Tree, XG Boost, Random Forest )

implementation of machine learning Algorithms such as decision tree and random forest and xgboost on darasets then compare results for each and implement ant colony and genetic algorithms on tsp map,

Mohamadreza Rezaei 1 Jan 19, 2022
PySpark + Scikit-learn = Sparkit-learn

Sparkit-learn PySpark + Scikit-learn = Sparkit-learn GitHub: https://github.com/lensacom/sparkit-learn About Sparkit-learn aims to provide scikit-lear

Lensa 1.1k Jan 04, 2023
Python module for data science and machine learning users.

dsnk-distributions package dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calcu

Emmanuel ASIFIWE 1 Nov 23, 2021
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification Introduction. This package includes the pyth

5 Dec 06, 2022
Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

2.5k Dec 28, 2022
MLflow App Using React, Hooks, RabbitMQ, FastAPI Server, Celery, Microservices

Katana ML Skipper This is a simple and flexible ML workflow engine. It helps to orchestrate events across a set of microservices and create executable

Tom Xu 8 Nov 17, 2022
Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

Payment-Date-Prediction Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

15 Sep 09, 2022
Confidence intervals for scikit-learn forest algorithms

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

272 Dec 01, 2022