chemometrics-ml-selection

name: chemometrics-ml-selection description: Expert guidance for selecting appropriate machine learning methods for chemometrics applications including spectroscopy, chromatography, and process analytics. Provides decision frameworks based on data characteristics, problem type, and domain best practices. license: MIT metadata: skill-author: Alban Ott based-on: Trinh et al. 2021 - Machine Learning in Chemical Product Engineering

Chemometrics ML Method Selection

Overview

Expert guidance for selecting ML methods for chemometrics applications. Covers algorithm choice based on data characteristics, problem type, and domain requirements. Chemometrics data is typically high-dimensional, small-sample, multicollinear, and interpretability-sensitive.

When to Use This Skill

Use when starting a new chemometrics analysis, working with spectroscopic or chromatographic data, building calibration/classification models, dealing with small datasets (n < 100), or comparing ML approaches systematically.

Decision Tree for Method Selection

START: What is your problem type?
|
+- REGRESSION (predict continuous values: concentration, properties)
|  |
|  +- Linear relationship expected?
|  |  +- YES -> Start with PLS or PCR
|  |  |         * PLS: When X and y should both be modeled
|  |  |         * PCR: When only X structure matters
|  |  |         * Consider: Ridge/Lasso for feature selection
|  |  |
|  |  +- NO -> Try non-linear methods
|  |            * Small data (n<100): Gaussian Processes (GP), SVM with RBF
|  |            * Large data (n>1000): Neural Networks, Random Forest
|  |            * Need interpretability: Tree-based (RF, Gradient Boosting)
|  |
|  +- How many samples do you have?
|     +- n < 50: GP, SVM, k-NN, or stay with PLS
|     +- 50 < n < 500: SVM, RF, GP, Neural Networks (small)
|     +- n > 500: Neural Networks, Deep Learning, Gradient Boosting
|
+- CLASSIFICATION (predict categories: pass/fail, species, authenticity)
|  |
|  +- How many samples do you have?
|  |  +- n < 50: PLS-DA, SVM, k-NN
|  |  +- 50 < n < 500: SVM, Random Forest, PLS-DA
|  |  +- n > 500: Neural Networks, Gradient Boosting
|  |
|  +- Need probabilistic outputs?
|     +- YES -> Logistic Regression, SVM with probability, RF
|     +- NO -> SVM, k-NN, PLS-DA
|
+- CLUSTERING (find natural groups: sample similarity, outlier detection)
|  |
|  +- Know number of clusters?
|  |  +- YES -> K-Means, GMM
|  |  +- NO -> Hierarchical Clustering, DBSCAN
|  |
|  +- Need soft assignments (probabilities)?
|     +- YES -> Gaussian Mixture Models (GMM)
|     +- NO -> K-Means, Hierarchical
|
+- DIMENSIONALITY REDUCTION (visualization, exploratory analysis)
   |
   +- Preserve variance -> PCA
   +- Preserve distances -> MDS, Isomap
   +- Visualization (2D/3D) -> t-SNE, UMAP
   +- With class information -> LDA, PLS-DA scores

Core Methods (Summary)

PLS: Linear, high-dim, n=20-200. Handles multicollinearity. VIP for interpretation. Details: references/method-details.md

SVM/SVR: Non-linear via kernels, n<100. Robust to outliers. Requires scaling. Details: references/method-details.md

Random Forest: Non-linear, n>50. Feature importance. No scaling needed. Details: references/method-details.md

Gaussian Processes: Very small data (n<50). Uncertainty quantification. O(n^3). Details: references/method-details.md

Neural Networks: Large data (n>500). Complex patterns. Needs regularization. Details: references/method-details.md

k-NN: Baseline model. Local patterns. Lazy learning. Requires scaling. Details: references/method-details.md

Method Comparison Table

Method	Best for	Data Size	Interpretability	Handles Multicollinearity	Requires Scaling
PLS	Linear, high-dim	Small-Med (20-200)	High	Yes	Optional
PCR	Linear, dim. reduction	Small-Med	High	Yes	Optional
SVM	Non-linear, small data	Small (<100)	Low	Moderate	Required
Random Forest	Complex patterns	Med-Large (>50)	Medium	Yes	No
Gradient Boosting	Maximum performance	Med-Large (>100)	Medium	Yes	No
GP	Very small, uncertainty	Very Small (<50)	Medium	Moderate	Required
Neural Networks	Complex, large data	Large (>500)	Low	Moderate	Required
k-NN	Baseline, local	Small-Med	High	No	Required

Decision Heuristics

By Data Characteristics

High-dimensional (p >> n): PLS, PCR (dimensionality reduction first)
Multicollinear features: PLS, Random Forest, Ridge Regression
Non-linear relationships: SVM with RBF kernel, Random Forest, Neural Networks
Mixed data types: Random Forest, Gradient Boosting
Need uncertainty estimates: Gaussian Processes, Bayesian approaches

By Problem Requirements

Maximum interpretability: PLS, Linear Regression, Decision Trees
Maximum performance: Gradient Boosting, Neural Networks (if enough data)
Robust to outliers: SVM (soft margin), Random Forest
Fast prediction: k-NN, Linear models (avoid GP for large test sets)
Probabilistic outputs: Logistic Regression, SVM with probability, Gaussian Processes

By Sample Size

See ../chemometrics-shared/references/sample-size-guidance.md for detailed guidance.

Workflow and Common Pitfalls

Standard workflow (Start Simple -> Non-linear -> Compare -> Tune -> Validate) and common pitfalls (complex models on small data, forgetting scaling, ignoring CV, default hyperparameters). Details: references/workflow-comparison.md

Advanced Topics

Ensemble methods, feature selection with models, and transfer learning for small datasets. Details: references/method-details.md

References

This skill is based on:

Trinh et al. (2021). "Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers." Processes, 9(8), 1456. doi:10.3390/pr9081456

Additional recommended reading:

Brereton, R. G. (2015). Chemometrics for Pattern Recognition. Wiley.
Wold, S., Sjostrom, M., & Eriksson, L. (2001). "PLS-regression: a basic tool of chemometrics." Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130.
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Packt Publishing.