Deep Forest (DF21) is a Python library implementing the Deep Forest ensemble learning algorithm, which serves as an alternative to deep neural networks for tabular data. The system provides both classification (CascadeForestClassifier) and regression (CascadeForestRegressor) capabilities through a cascade of decision tree ensembles. Each cascade layer consists of multiple estimators (Random Forest and Extra Trees) whose out-of-bag predictions serve as augmented features for subsequent layers. The architecture includes feature binning for efficiency, early stopping mechanisms, support for custom estimators/predictors, and a partial mode for memory-efficient training on large datasets. The codebase is organized into modular components with clear separation of concerns, including dedicated modules for binning, estimation, layer management, tree implementation, and I/O operations.
ChefBoost is a Python library implementing decision tree algorithms (ID3, C4.5, CART, CHAID, regression) and ensemble methods (AdaBoost, Gradient Boosting, Random Forest). It provides a simple API for building models from pandas DataFrames and making predictions. The library handles both categorical and numerical features without requiring preprocessing. Architecture consists of modular components: main interface (Chefboost.py), utilities (commons/), core training algorithms (training/), and ensemble methods (tuning/). Key strengths include proper packaging, unit tests, documentation, and solutions to technical challenges like recursive multiprocessing. The implementation correctly follows standard ML algorithms while providing a usable framework for developers.
This repository implements an end-to-end predictive modeling pipeline for Premier League football match outcomes (home win, draw, away win). It ingests raw match statistics from the api-football.com REST API, processes JSON data into structured DataFrames, engineers features using rolling averages (5- and 10-game windows) and team-vs-opponent differentials, trains multiple classifiers (Random Forest, SVM, KNN, MLP) with hyperparameter tuning, and serves live predictions via a Flask web application. The dominant character is data research: the primary deliverable is a set of trained models and an analytical pipeline, with the web server being a secondary deployment artifact. The best-performing model is a Random Forest achieving ~50-51% test accuracy, demonstrating that football outcomes are modestly predictable from historical statistics. The codebase includes modular helper modules for feature engineering, data scaling, and evaluation plotting that are reusable for other sports or time-series classification tasks. However, the project suffers from hardcoded absolute paths, outdated dependencies (Python 3.7, sklearn 0.23.1), plain-text API keys in the repository, and a static dataset that will drift over time.