Simple linear model implementations from scratch.

Last update: Sep 13, 2021

Related tags

Machine Learning hand_crafted_models

Overview

Hand Crafted Models

Simple linear model implementations from scratch.

Table of contents

Overview
Project Structure
Getting started
Citing this project

1. Overview

The repo consists of popular predictive linear models (i.e., linear and logistic regressions) and is intended for educational purposes to demonstrate the mathematics behind the models.

2. Project Structure

hand_crafted_models/
    activations.py          ...Activation function implementations
    linear_regression.py    ...Linear regression implementations
    logistic_regression.py  ...Logistic regression implementation
    loss_functions.py       ...Loss function implementations
    optimization.py         ...Optimizer implementations
    utils.py                ...Commonly used functions
requirements.txt            ...project dependencies
setup.py

3. Getting Started

Clone the project

git clone https://github.com/sadighian/hand_crafted_models.git

Create a virtual environment

cd hand_crafted_models          # Change to project directory
virtualenv -p python3 venv      # Create the virtual environment
source venv/bin/activate        # Start using the venv

Install the project and its dependencies

pip3 install -e .               # Execute command inside install directory

Fit linear models with sample data

python3 tests/tests.py

4. Citing the Project

@misc{Hand Crafted Models,
    author = {Jonathan Sadighian},
    title = {Hand Crafted Models: Simple linear model implementations},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/sadighian/hand_crafted_models}},
}

Simple linear model implementations from scratch.

Related tags

Overview

Hand Crafted Models

1. Overview

2. Project Structure

3. Getting Started

4. Citing the Project

Owner

Jonathan Sadighian

It is a forest of random projection trees

Houseprices - Predict sales prices and practice feature engineering, RFs, and gradient boosting

customer churn prediction prevention in telecom industry using machine learning and survival analysis

MICOM is a Python package for metabolic modeling of microbial communities

This repo includes some graph-based CTR prediction models and other representative baselines.

A data preprocessing and feature engineering script for a machine learning pipeline is prepared.

pure-predict: Machine learning prediction in pure Python

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

monolish: MONOlithic Liner equation Solvers for Highly-parallel architecture

A benchmark of data-centric tasks from across the machine learning lifecycle.

Client - 🔥 A tool for visualizing and tracking your machine learning experiments

Simulate & classify transient absorption spectroscopy (TAS) spectral features for bulk semiconducting materials (Post-DFT)

Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

Python Research Framework

Decision Tree Regression algorithm implemented on Python from scratch.

Data from "Datamodels: Predicting Predictions with Training Data"

pymc-learn: Practical Probabilistic Machine Learning in Python

ML Kaggle Titanic Problem using LogisticRegrission

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.