.. _sklearn:

**Getting started with Scikit-Learn**
=====================================

This section describes the basics of working with scikit-learn. You'll learn about scikit-learn's key features, models, and methods.

What is Scikit-Learn?
---------------------

`Scikit-learn <https://scikit-learn.org/>`_ (``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 two-step process:

1.  **Fit**: The model learns patterns from training data using ``fit()``
2.  **Apply**: The fitted model processes new data using either:

- ``transform()`` for preprocessing steps and unsupervised learning
- ``predict()`` for supervised learning models (classification and regression)

First, import the required modules:

.. code-block:: python

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

Preprocessing with ``StandardScaler``:

.. code-block:: python

    # Create and fit the scaler
    scaler = StandardScaler()
    scaler.fit(X)

    # Transform the data
    X_scaled = scaler.transform(X)

    # For new data
    X_new_scaled = scaler.transform(X_new)

Building a ``PLSRegression`` model:

.. code-block:: python

    # Create and fit the PLS model
    pls = PLSRegression(n_components=2)
    pls.fit(X_scaled, y)

    # Make predictions
    y_pred = pls.predict(X_new_scaled)

Why Use Scikit-Learn for Chemometrics?
--------------------------------------

* **Flexibility**: Works with various chemometric models and preprocessing meth``numpy``:ods.
* **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.