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 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