This repository contains detailed documentation and implementations of various topics within scikit-learn for machine learning. The topics covered include:
- Estimators
- Custom Estimators
- Mixins
- Transformers
- Custom Transformers
- Composite Transformers
- Column Transformers
- Feature Unions
- Pipelines
Each section provides a comprehensive explanation of the concepts, their significance in machine learning, and how they are used in practice.
In scikit-learn, an estimator is any object that learns from data. Estimators implement the fit method, which takes a dataset as an argument and adjusts the internal parameters of the estimator according to the data. Common examples include classifiers, regressors, and clustering algorithms.
Estimators are central to scikit-learn's design. They provide a consistent interface for a wide range of machine learning algorithms, making it easy to experiment with different models.
- Classifier: An estimator that classifies data into categories (e.g.,
LogisticRegression). - Regressor: An estimator that predicts a continuous output (e.g.,
LinearRegression). - Clusterer: An estimator that assigns data points to clusters (e.g.,
KMeans).
Custom estimators allow you to create your own machine learning models by extending scikit-learn's base classes. This is useful when you need functionality not provided by the built-in estimators.
Creating custom estimators can help tailor models to specific problems or data characteristics, allowing for more flexibility and potentially better performance.
- Inherit from
BaseEstimatorandClassifierMixinorRegressorMixin. - Implement the
fitandpredictmethods.
Mixins are classes that provide additional methods to a class through multiple inheritance. In scikit-learn, mixins add specific functionalities to custom estimators or transformers.
Mixins enhance the functionality of custom objects without requiring extensive changes to the base class. They provide modular and reusable code.
- ClassifierMixin: Adds methods specific to classifiers.
- RegressorMixin: Adds methods specific to regressors.
- TransformerMixin: Adds methods specific to transformers.
Transformers are estimators that transform data. They implement the fit and transform methods, where fit learns parameters from the data, and transform applies the transformation.
Transformers are used for preprocessing data, feature extraction, and feature engineering. They prepare data for model training and improve model performance.
- StandardScaler: Standardizes features by removing the mean and scaling to unit variance.
- PCA: Performs Principal Component Analysis for dimensionality reduction.
Custom transformers allow you to define your own data transformation logic. Like custom estimators, custom transformers inherit from scikit-learn base classes and implement the fit and transform methods.
Custom transformers enable you to apply domain-specific preprocessing and feature engineering steps that are not available in scikit-learn's built-in transformers.
- Inherit from
BaseEstimatorandTransformerMixin. - Implement the
fitandtransformmethods.
Composite transformers combine multiple transformers into a single transformer. This allows for complex preprocessing pipelines where multiple steps are applied sequentially.
Composite transformers enable modular and reusable preprocessing pipelines, improving code organization and maintainability.
- Pipeline: Chains multiple transformers and an estimator.
- FeatureUnion: Combines outputs of multiple transformers.
Column transformers apply different transformations to different subsets of features. This is useful when different types of features require different preprocessing steps.
Column transformers simplify the preprocessing of heterogeneous data by applying appropriate transformations to each subset of features.
- ColumnTransformer: Applies transformers to specified columns.
Feature unions combine multiple transformer outputs into a single dataset. Each transformer is applied independently, and their outputs are concatenated.
Feature unions allow for parallel feature processing, enabling complex feature engineering workflows that combine multiple sources of information.
- FeatureUnion: Combines multiple transformer outputs.
Pipelines chain multiple steps (transformers and estimators) into a single object. The output of each step is passed as input to the next step.
Pipelines ensure that all preprocessing and modeling steps are applied consistently and in the correct order. They also make it easy to grid search over preprocessing parameters.
- Pipeline: Chains multiple steps into a single estimator.
Please refer to the detailed documentation and code examples in each corresponding notebook to explore these concepts further.