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Introduction

  • Quick Start Guide
  • Citations
  • Contributing

API Reference

  • pychemauth
    • pychemauth package
      • pychemauth.analysis package
      • pychemauth.classifier package
      • pychemauth.eda package
      • pychemauth.manifold package
      • pychemauth.preprocessing package
      • pychemauth.regressor package

Tutorial

  • API Examples
    • Exploratory Data Analysis (EDA)
    • Using Pipelines
    • Imputing Missing Values and Those Below LOD
    • Dealing with Class Imbalance
    • Exploring with PCA
    • Regression with PLS
    • Classification with PLS-DA
    • Authentication with DD-SIMCA
    • Authentication with EllipticManifold (including UNEQ)
    • Classification Under Open Set Conditions
    • Selecting the Most Important Features
    • Comparing the Performance of Pipelines
    • Inspecting and Explaining Models
    • Classifying 1D Spectra with Convolutional Neural Networks (CNNs)
    • Should I Collect More Data?
    • Saving and Sharing Models
  • Real Applications
    • Building Material Authenticators using Prompt Gamma Ray Activation Analysis
    • Interpretable Machine Learning Models of Chemometric Trends in Seabirds of the North Pacific Ocean

Learn

  • Chemometric Carpentry
  • Conventional Chemometric Tools
    • Ordinary Least Squares Regression (OLS)
    • Principal Components Analysis (PCA) and Principal Component Regression (PCR)
    • Projection to Latent Structures (PLS)
    • Linear Discriminant Analysis
    • Partial Least Squares - Discriminant Analysis (PLS-DA)
    • Soft Independent Modeling of Class Analogies (SIMCA)
  • Cross Validation
    • Optimizing Models with Cross-Validation
    • Statistical Comparison between Models using Cross-Validation

Python Module Index

p
 
p
- pychemauth
    pychemauth.analysis
    pychemauth.classifier
    pychemauth.eda
    pychemauth.manifold
    pychemauth.preprocessing
    pychemauth.regressor

By Nathan A. Mahynski

© Copyright 2024, Nathan A. Mahynski.