Principle:Scikit learn Scikit learn Model Instantiation

Field	Value
sources	Buitinck, L. et al. (2013). API design for machine learning software: experiences from the scikit-learn project. ECML PKDD Workshop: Languages for Data Mining and Machine Learning; scikit-learn documentation: https://scikit-learn.org/stable/developers/develop.html
domains	Machine_Learning, Software_Engineering
last_updated	2026-02-08 15:00 GMT

Overview

A design pattern that configures a machine learning estimator with hyperparameters before training.

Description

In scikit-learn, every machine learning algorithm is represented as a Python class that follows the estimator pattern. Model instantiation is the act of creating an instance of such a class, passing hyperparameters as constructor arguments. This step configures how the model will learn but does not yet perform any learning.

The estimator pattern is enforced through the BaseEstimator base class, which provides:

• get_params(deep=True) -- Returns a dictionary of the estimator's hyperparameters and their current values. This enables introspection, serialization, and use within meta-estimators such as GridSearchCV.
• set_params(**params) -- Sets hyperparameters by name, allowing programmatic reconfiguration. This is used internally by hyperparameter search utilities.

The key design rule is that every constructor parameter must be stored as an instance attribute with the same name. For example, if the constructor accepts C=1.0, the instance must have self.C = 1.0. No validation or transformation of hyperparameters is performed in the constructor; validation is deferred to the fit method.

Usage

Use model instantiation when:

• Configuring a classifier or regressor -- Set hyperparameters such as regularization strength, solver algorithm, convergence tolerance, and maximum iterations.
• Building pipelines -- Instantiated estimators are composed into Pipeline objects, where each step is an estimator instance.
• Hyperparameter search -- Tools like GridSearchCV use set_params to reconfigure estimators across different hyperparameter combinations.
• Reproducibility -- Explicitly setting random_state during instantiation ensures deterministic behavior across runs.

Theoretical Basis

Separation of Configuration from Execution

The estimator pattern embodies a strict separation between configuration (constructor) and execution (fit/predict/transform). This separation has several benefits:

• Declarative specification -- The constructor call serves as a complete, human-readable specification of the model's configuration. No hidden state is introduced.
• Cloning -- The sklearn.base.clone function creates a new estimator with the same hyperparameters but without fitted state, which is essential for cross-validation where a fresh model must be trained on each fold.
• Introspection -- The get_params / set_params protocol enables generic tools to manipulate any estimator without knowing its specific class.

Reproducibility

Many algorithms involve randomness (random initialization, stochastic optimization, data sampling). By accepting a random_state parameter at instantiation time, scikit-learn allows users to fix the random seed, ensuring that repeated runs produce identical results. This is critical for scientific reproducibility and debugging.

Hyperparameters vs. Learned Parameters

It is important to distinguish between:

• Hyperparameters -- Set by the user before training (e.g., C, max_iter, solver). These are arguments to the constructor.
• Learned (fitted) parameters -- Estimated from data during fit (e.g., coef_, intercept_). By convention, these are stored as attributes with a trailing underscore.

Related Pages

• Implementation:Scikit_learn_Scikit_learn_LogisticRegression_Init