Welcome to the `aiqclib` documentation! ======================================= ``aiqclib`` is a Python library that provides a configuration-driven workflow for machine learning, simplifying dataset preparation, model training, and data classification. It is a core component of the AIQC project. .. note:: To unlock the full potential of your model building processes, comprehensive configuration is key. For a streamlined initial test or to explore the library with base settings, see the :doc:`how-to/quick_start` guide. Workflow of `aiqclib` --------------------------------------------- The library is designed around a three-stage workflow: 1. **Dataset Preparation:** Prepare feature datasets from raw data and generate training, validation, and test data sets. 2. **Training & Evaluation:** Train machine learning models and evaluate their performance using cross-validation. 3. **Classification:** Apply a trained model to classify new, unseen data. Each stage is controlled by a YAML configuration file, allowing you to define and reproduce your entire workflow with ease. Algorithms supported by `aiqclib` --------------------------------------------- ================================ ================================= ============ ====================== Category Algorithm Short Name Method ================================ ================================= ============ ====================== Tree-Based & Ensemble **XGBoost** XGB Ensemble (Boosting) \ **Random Forest** RF Ensemble (Bagging) \ **Decision Tree** DT Tree Linear & Geometric **Logistic Regression** Logit Linear \ **Linear Discriminant Analysis** LDA Linear / Statistical \ **Support Vector Machine** SVM Geometric Instance-Based **K-Nearest Neighbors** KNN Distance-based Probabilistic **Gaussian Naive Bayes** GNB Probabilistic Neural Network **Multilayer Perceptron** MLP Neural Network ================================ ================================= ============ ====================== Project links --------------------------------------------- - `GitHub Repository `_ - `Issue Tracker `_ - `Pull Requests `_ ---------- These tutorials provide a step-by-step guide to the core workflows of the library. If you are new to ``aiqclib``, start here. .. toctree:: :maxdepth: 2 :caption: 📘 Getting Started tutorial/overview tutorial/installation tutorial/preparation tutorial/training tutorial/classification ---------- This section provides practical examples and solutions for common tasks related to using ``aiqclib``. .. toctree:: :maxdepth: 2 :caption: 💡 How-To Guides how-to/quick_start how-to/data_preprocessing_utilities how-to/algorithm_selection how-to/prediction_threshold how-to/performance_evaluation how-to/shap_values how-to/down_sampling_negative how-to/feature_normalization how-to/selecting_specific_configurations ---------- This section provides detailed reference information for all parameters in the YAML configuration files. .. toctree:: :maxdepth: 2 :caption: ⚙️ Configuration configuration/preparation configuration/training configuration/classification ---------- Understanding the input features is crucial for building effective models. This section provides a detailed explanation of each feature used for model training. .. toctree:: :maxdepth: 2 :caption: 📊 Features features/location features/day_of_year features/basic_values features/profile_summary_stats features/neigbouring_values ---------- For in-depth information on specific functions, classes, and methods, consult the API documentation. .. toctree:: :maxdepth: 4 :caption: 🧩 API Reference api/modules