Why Scikit-Learn?#

Scikit-Learn brings modern machine learning tools to chemometrics—making workflows more flexible, scalable, and easy to prototype and validate.

  • Flexibility: Works with various chemometric models and preprocessing methods.

  • Integration: Can be combined with domain-specific tools for spectral analysis, multivariate statistics, and experimental design.

  • Scalability: Can handle small laboratory datasets as well as large-scale industrial data.

  • Easy Experimentation: Rapid prototyping with different algorithms and preprocessing techniques.

What is Scikit-Learn?#

Scikit-learn (sklearn) is an open-source machine-learning library built on top of NumPy, SciPy. It offers a unified API for various machine-learning techniques, including:

  • Supervised learning: Regression, classification, and ensemble methods

  • Unsupervised learning: Clustering and dimensionality reduction

  • Data preprocessing: Feature scaling, encoding, and transformation

  • Model evaluation: Cross-validation, metrics, and hyperparameter tuning

Scikit-learn is widely used in research and industry due to its simplicity, efficiency, and extensive documentation.

A Broad Toolbox of Models and Methods#

One of the biggest strengths of scikit-learn is its vast selection of models and tools. Some key categories include:

Regression#

  • Linear Regression (LinearRegression): A simple yet effective model for continuous target variables.

  • Partial Least Squares (PLSRegression): A powerful method often used in chemometrics.

  • Support Vector Regression (SVR): Effective for nonlinear relationships.

Classification#

  • Logistic Regression (LogisticRegression): For binary and multi-class classification.

  • Random Forest (RandomForestClassifier): A robust ensemble method for better accuracy.

  • Support Vector Machines (SVC): Effective for complex decision boundaries.

Dimensionality Reduction#

  • Principal Component Analysis (PCA): Extracts the most important features in your dataset.

  • Non Negative Matrix Factorization (NMF): Useful for signal decomposition.

  • t-SNE (TSNE): Helps visualize complex high-dimensional data.

Preprocessing Tools#

  • Feature Scaling (StandardScaler, MinMaxScaler): Essential for models that rely on distance metrics.

  • Encoding Categorical Data (OneHotEncoder, LabelEncoder): Transforms categorical variables into numerical format.

  • Feature Selection (SelectKBest, RFE): Helps choose the most relevant variables.

The scikit-learn workflow#

Scikit-learn follows an intuitive three-step process outlined in the figure below:

scikit-learn workflow

  1. Define: Configure the model/preprocessing parameters.

  2. Fit: The model learns patterns from training data using fit()

  3. Apply: The fitted model processes new data using either:

  • transform() for preprocessing steps and unsupervised learning

  • predict() for supervised learning models (classification and regression)

Let’s see two examples, one for a preprocessing and one for a regerssion model. First, import the required modules:

from sklearn.preprocessing import StandardScaler
from sklearn.cross_decomposition import PLSRegression

Preprocessing with StandardScaler:

# 1. Define the preprocessor
scaler = StandardScaler(with_mean=True, with_std=False)

# 2. Fit the preprocessor to the data
scaler.fit(X)

# 3. Apply the processor to the data
X_scaled = scaler.transform(X)

# ... or to new new data
X_new_scaled = scaler.transform(X_new)

Building a PLSRegression model:

# 1. Define the PLS model with two components
pls = PLSRegression(n_components=2)

# 2. Fit the PLS model to the training data
pls.fit(X_scaled, y)

# 3. Apply the PLS model to new data
y_pred = pls.predict(X_new_scaled)