scikitlearn
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scikitlearn  

GitHub Repository  
Developer  scikitlearn 
Written in  Python 
Stars  58k 
Watchers  2.1k 
Created  08/17/2010 
Last updated  04/03/2024 
License  BSD 3Clause "New" or "Revised" 
Homepage  scikitlearn.org 
Repository  scikitlearn/scikitlearn 
Auto Wiki  
Revision  
Software Version  0.0.8Basic 
Generated from  Commit 30f4d9 
Generated at  04/04/2024 
The scikitlearn library is a powerful and flexible machine learning toolkit written in Python. It provides a wide range of tools and algorithms for data preprocessing, model training, and performance evaluation, making it a valuable resource for data scientists and machine learning practitioners.
The core functionality of the library is implemented across several key directories, each focusing on a specific aspect of the machine learning workflow:

The
…/preprocessing
directory contains a comprehensive set of tools for data preprocessing, including scaling, normalization, encoding, and feature engineering. This includes classes likeStandardScaler
,OneHotEncoder
, andPolynomialFeatures
, which allow users to transform their data into a format suitable for machine learning models. 
The
…/linear_model
directory provides a variety of linear models, such as Generalized Linear Models, Bayesian Linear Models, Logistic Regression, and Stochastic Gradient Descentbased models. These models are widely used for classification, regression, and other tasks, and the directory includes over 20 different classes and functions implementing these algorithms. 
The
…/model_selection
directory is a crucial component of the scikitlearn library, offering tools for model selection, hyperparameter tuning, and performance evaluation. This includes crossvalidation techniques, grid search, randomized search, and learning curve visualization. TheGridSearchCV
andRandomizedSearchCV
classes, for example, allow users to efficiently tune the hyperparameters of their models. 
The
…/manifold
directory contains implementations of dimensionality reduction and data embedding techniques, such as Isomap, Locally Linear Embedding, Multidimensional Scaling, Spectral Embedding, and tSNE. These algorithms can be used to visualize and analyze highdimensional data by projecting it into a lowerdimensional space. 
The
…/neighbors
directory provides functionality for nearest neighborsrelated algorithms, including kNearest Neighbors classification and regression, radiusbased nearest neighbors, kernel density estimation, and outlier detection using the Local Outlier Factor (LOF) algorithm. 
The
…/feature_selection
directory offers a range of feature selection techniques, such as univariate feature selection, recursive feature elimination, and feature selection based on model importance. These tools can be used to identify the most relevant features in a dataset, which is crucial for improving model performance and interpretability. 
The
…/inspection
directory contains functionality for inspecting and understanding machine learning models, including partial dependence plots, permutation importance, and decision boundary visualization. These tools can help users gain insights into the behavior and performance of their models. 
The
…/impute
directory provides various imputation methods for handling missing values in datasets, including simple imputation, iterative imputation, and kNearest Neighborsbased imputation.
The scikitlearn library is designed with a focus on flexibility, efficiency, and ease of use. The modular structure of the codebase, with specialized submodules for different machine learning tasks, allows users to easily access the functionality they need for their specific use cases. Additionally, the library's comprehensive test suite and welldocumented code ensure the reliability and robustness of the implemented algorithms.
Data PreprocessingRevise
References: sklearn/preprocessing
The scikitlearn library provides a comprehensive set of tools for data preprocessing, including scaling, normalization, encoding, and feature engineering. These tools are implemented across several directories and modules, allowing users to easily prepare their data for machine learning models.
Scaling and NormalizationRevise
References: sklearn/preprocessing
The scikitlearn library provides several classes and functions for scaling and normalizing data, including StandardScaler
, MinMaxScaler
, MaxAbsScaler
, RobustScaler
, and Normalizer
.
Binarization and DiscretizationRevise
The Binarizer
class in the scikitlearn/sklearn/preprocessing
module is used to binarize data by applying a threshold to the input values. It can be useful for converting continuous features into binary (0/1) features.
EncodingRevise
References: sklearn/preprocessing
, sklearn/preprocessing/_encoders.py
, sklearn/preprocessing/_label.py
The scikitlearn library provides two main classes for encoding categorical features: OneHotEncoder
and OrdinalEncoder
. These classes are implemented in the …/_encoders.py
file.
TransformationsRevise
The PowerTransformer
and QuantileTransformer
classes in the scikitlearn/sklearn/preprocessing
module provide functionality for applying power and quantile transformations to the input data, respectively. Additionally, the FunctionTransformer
class allows users to apply arbitrary functions to the input data as part of a preprocessing pipeline.
Polynomial and Spline FeaturesRevise
The PolynomialFeatures
class in the scikitlearn/sklearn/preprocessing
directory is used to generate polynomial and interaction features from input data. It can create features up to a specified degree, including interaction terms and an optional bias term. The transform()
method of this class uses an efficient implementation for sparse input data, leveraging the _csr_polynomial_expansion()
function.
Target EncodingRevise
The TargetEncoder
class in the …/_target_encoder.py
file provides a way to encode categorical features based on the target variable. This can be useful for improving the performance of machine learning models, especially when dealing with highcardinality categorical features.
Model TrainingRevise
References: sklearn/linear_model
, sklearn/ensemble
, sklearn/svm
, sklearn/cluster
, sklearn/gaussian_process
The scikitlearn library offers a wide range of machine learning models for classification, regression, and clustering tasks, implemented across several directories. The core functionality of these models is as follows:
Generalized Linear Models (GLMs)Revise
References: sklearn/linear_model/_glm
The …/_glm
directory contains the implementation of Generalized Linear Models (GLMs) in the scikitlearn library. It provides several classes that allow for fitting and predicting using GLMs with different underlying distributions, such as Poisson, Gamma, and Tweedie distributions.
Bayesian Linear ModelsRevise
References: sklearn/linear_model/_bayes.py
The _bayes.py
file in the scikitlearn library's linear_model
module contains two main classes: BayesianRidge
and ARDRegression
. These classes implement Bayesian regression techniques, specifically Bayesian Ridge Regression and Automatic Relevance Determination (ARD) Regression.
Robust Linear ModelsRevise
References: sklearn/linear_model/_huber.py
The HuberRegressor
class in …/_huber.py
implements a robust linear regression model that is less sensitive to outliers in the data. The Huber Regressor optimizes a loss function that is quadratic for small residuals (the difference between the predicted and actual values) and linear for large residuals, allowing the model to be more robust to outliers.
Least Angle RegressionRevise
References: sklearn/linear_model/_least_angle.py
The scikitlearn/sklearn/linear_model/_least_angle.py
file contains the implementation of the Least Angle Regression (LARS) algorithm and its variants, including Lasso and CrossValidated LARS and Lasso models.
Logistic RegressionRevise
References: sklearn/linear_model/_logistic.py
The LogisticRegression
class in the …/_logistic.py
file implements the Logistic Regression algorithm for binary and multiclass classification problems. It supports various regularization penalties ('l1'
, 'l2'
, 'elasticnet'
) and optimization solvers ('liblinear'
, 'lbfgs'
, 'newtoncg'
, 'newtoncholesky'
, 'sag'
, 'saga'
).
Orthogonal Matching PursuitRevise
References: sklearn/linear_model/_omp.py
The scikitlearn
library provides an implementation of the Orthogonal Matching Pursuit (OMP) algorithm, which is a greedy algorithm for solving sparse linear regression problems. The core functionality of the OMP algorithm is implemented in the …/_omp.py
file.
Passive Aggressive AlgorithmsRevise
References: sklearn/linear_model/_passive_aggressive.py
The PassiveAggressiveClassifier
and PassiveAggressiveRegressor
classes in the …/_passive_aggressive.py
file implement the Passive Aggressive algorithm for classification and regression tasks, respectively.
PerceptronRevise
References: sklearn/linear_model/_perceptron.py
The Perceptron
class in …/_perceptron.py
provides a simple and efficient implementation of the perceptron algorithm, a type of linear classifier. The Perceptron
class is a wrapper around the BaseSGDClassifier
class, with the loss
parameter set to "perceptron"
and the learning_rate
parameter set to "constant"
.
Quantile RegressionRevise
References: sklearn/linear_model/_quantile.py
The QuantileRegressor
class in the …/_quantile.py
file implements a linear regression model that predicts conditional quantiles, rather than the mean, which is the typical target of linear regression. This can be useful in applications where the mean may not be the most informative statistic.
RANSAC RegressionRevise
References: sklearn/linear_model/_ransac.py
The RANSACRegressor
class in the …/_ransac.py
file implements the RANSAC (Random Sample Consensus) algorithm for robust regression. RANSAC is an iterative method for estimating parameters from a data set containing outliers.
Ridge RegressionRevise
References: sklearn/linear_model/_ridge.py
The Ridge
and RidgeClassifier
classes in the …/_ridge.py
file provide an implementation of Ridge regression and Ridge classification, respectively.
Stochastic Gradient DescentRevise
The scikitlearn library provides an efficient and scalable implementation of Stochastic Gradient Descent (SGD) based models, including Ridge Regression, Logistic Regression, and OneClass SVM. These models are implemented in the _sag.py
and _stochastic_gradient.py
files within the linear_model
module.
TheilSen RegressionRevise
References: sklearn/linear_model/_theil_sen.py
The TheilSenRegressor
class in the …/_theil_sen.py
file implements the TheilSen Estimator, a robust multivariate regression algorithm. The TheilSen Estimator is known for its high breakdown point, making it resistant to outliers in the data.
Model Selection and EvaluationRevise
References: sklearn/model_selection
The scikitlearn library provides a comprehensive set of tools for model selection, hyperparameter tuning, and performance evaluation, implemented in the …/model_selection
directory.
CrossValidationRevise
The scikitlearn library provides a comprehensive set of tools for performing crossvalidation, which is a crucial technique for evaluating the performance of machine learning models. The core functionality is implemented in the …/_validation.py
module.
Hyperparameter OptimizationRevise
References: sklearn/model_selection/_search.py
, sklearn/model_selection/_search_successive_halving.py
, sklearn/model_selection/tests/test_search.py
, sklearn/model_selection/tests/test_successive_halving.py
The scikitlearn library provides two main classes for performing hyperparameter optimization: GridSearchCV
and RandomizedSearchCV
. These classes implement grid search and randomized search, respectively, which are two common techniques for tuning the hyperparameters of machine learning models.
Model EvaluationRevise
References: sklearn/model_selection/_validation.py
, sklearn/model_selection/tests/test_validation.py
The scikitlearn library provides a comprehensive set of tools for evaluating the performance of machine learning models, implemented in the sklearn.model_selection._validation
module.
VisualizationRevise
The scikitlearn library provides tools for visualizing model selection and evaluation, including the LearningCurveDisplay
and ValidationCurveDisplay
classes. These classes offer a convenient way to generate and customize visualizations of the learning curve and validation curve for machine learning models.
Dimensionality Reduction and Manifold LearningRevise
References: sklearn/manifold
The scikitlearn library provides algorithms for dimensionality reduction and manifold learning, implemented in the …/manifold
directory. This directory contains the implementation of various techniques, including:
IsomapRevise
References: sklearn/manifold/_isomap.py
The Isomap
class in the …/_isomap.py
file provides the core functionality for the Isomap algorithm, a nonlinear dimensionality reduction technique. Isomap is a manifold learning algorithm that preserves the geodesic distances between data points, allowing it to effectively capture the underlying nonlinear structure of highdimensional data.
Locally Linear Embedding (LLE)Revise
References: sklearn/manifold/_locally_linear.py
Locally Linear Embedding (LLE)
Multidimensional Scaling (MDS)Revise
References: sklearn/manifold/_mds.py
The MDS
class in the …/_mds.py
file implements Multidimensional Scaling (MDS), a technique for embedding highdimensional data into a lowerdimensional space. The core functionality is provided by the smacof()
function, which computes the MDS solution using the SMACOF (Scaling by MAjorizing a COmplicated Function) algorithm.
Spectral EmbeddingRevise
References: sklearn/manifold/_spectral_embedding.py
Spectral Embedding is a spectral clusteringbased dimensionality reduction algorithm implemented in the _spectral_embedding.py
file of the scikitlearn library. The main entry point is the spectral_embedding()
function, which takes an adjacency matrix as input and projects the samples onto the first eigenvectors of the graph Laplacian.
tSNE (tDistributed Stochastic Neighbor Embedding)Revise
References: sklearn/manifold/_t_sne.py
The TSNE
class in the scikitlearn/sklearn/manifold/_t_sne.py
file provides an implementation of the tDistributed Stochastic Neighbor Embedding (tSNE) algorithm, a popular nonlinear dimensionality reduction technique for embedding highdimensional data into a lowdimensional space.
Nearest Neighbors and Density EstimationRevise
References: sklearn/neighbors
The scikitlearn library includes functionality for nearest neighbors search, kernel density estimation, and outlier detection, implemented in the …/neighbors
directory.
kNearest Neighbors (kNN)Revise
References: sklearn/neighbors/_base.py
, sklearn/neighbors/_classification.py
, sklearn/neighbors/_regression.py
The kNearest Neighbors (kNN) algorithm is a nonparametric method used for classification and regression. It provides functionality for knearest neighbors search, including knearest neighbors classification and regression.
RadiusBased Nearest NeighborsRevise
References: sklearn/neighbors/_base.py
, sklearn/neighbors/_classification.py
, sklearn/neighbors/_regression.py
The RadiusNeighborsRegressor
and RadiusNeighborsClassifier
classes in the sklearn.neighbors
module of the scikitlearn library implement the radiusbased nearest neighbors algorithm. This is a variant of the knearest neighbors algorithm that uses a fixed radius to determine the neighbors, instead of a fixed number of neighbors.
Nearest Neighbor GraphRevise
References: sklearn/neighbors/_graph.py
The nearest neighbor graph functionality in the scikitlearn library provides tools for computing the weighted graph of knearest neighbors and neighbors within a given radius for a set of data points. This functionality is implemented in the …/_graph.py
file.
Kernel Density EstimationRevise
References: sklearn/neighbors/_kde.py
The sklearn/neighbors/_kde.py
file provides the implementation of Kernel Density Estimation (KDE) in the scikitlearn library. The main component is the KernelDensity
class, which allows users to fit a KDE model on a dataset and perform various operations such as scoring samples, computing the total loglikelihood, and generating random samples from the model.
Local Outlier Factor (LOF)Revise
References: sklearn/neighbors/_lof.py
The Local Outlier Factor (LOF) algorithm is an unsupervised outlier detection method that identifies outliers based on the local density of the data points. The core functionality of the LOF algorithm is implemented in the LocalOutlierFactor
class, located in the …/_lof.py
file.
Neighborhood Components Analysis (NCA)Revise
References: sklearn/neighbors/_nca.py
Neighborhood Components Analysis (NCA)
Nearest Centroid ClassifierRevise
References: sklearn/neighbors/_nearest_centroid.py
The Nearest Centroid Classifier is a simple and efficient classification algorithm that assigns a label to a new input based on the nearest centroid of the training data. The implementation of this algorithm is provided in the NearestCentroid
class in the …/_nearest_centroid.py
file.
Unsupervised Nearest NeighborsRevise
References: sklearn/neighbors/_unsupervised.py
The NearestNeighbors
class provides an unsupervised learner for implementing nearest neighbors searches, supporting various algorithms and distance metrics. It is the primary component in the …/_unsupervised.py
file.
Feature SelectionRevise
References: sklearn/feature_selection
The scikitlearn library offers a range of feature selection techniques, implemented in the …/feature_selection
directory. This directory includes various tools and algorithms for feature selection, which is the process of identifying and selecting the most relevant features from a dataset for use in model training.
Univariate Feature SelectionRevise
The _univariate_selection.py
file in the scikitlearn library provides a comprehensive set of tools for performing univariate feature selection. The main classes in this file are:
Recursive Feature Elimination (RFE)Revise
References: sklearn/feature_selection/_rfe.py
The Recursive Feature Elimination (RFE) algorithm is a feature selection technique implemented in the scikitlearn
library. It recursively removes features and builds a model on the remaining features, allowing you to select the most important features for your machine learning task.
Sequential Feature SelectionRevise
References: sklearn/feature_selection/_sequential.py
The SequentialFeatureSelector
class in the …/_sequential.py
file is responsible for implementing a sequential feature selection algorithm. This algorithm iteratively adds or removes features from the input data based on a specified scoring function and stopping criterion.
Feature Selection Based on Model ImportanceRevise
References: sklearn/feature_selection/_from_model.py
The SelectFromModel
class in the …/_from_model.py
file is a metatransformer that allows for feature selection based on the importance weights of an underlying estimator. This class can be used with any estimator that has a feature_importances_
or coef_
attribute after fitting, or with a custom importance getter function.
Mutual InformationRevise
References: sklearn/feature_selection/_mutual_info.py
The scikitlearn library provides functionality for estimating the mutual information between input features and the target variable, for both classification and regression problems. This is implemented in the …/_mutual_info.py
file.
Variance ThresholdRevise
References: sklearn/feature_selection/_variance_threshold.py
The VarianceThreshold
class, which is part of the scikitlearn/sklearn/feature_selection/
directory, is a feature selection algorithm that removes lowvariance features from the input data. This algorithm is useful for unsupervised learning tasks, as it only considers the features (X) and not the desired outputs (y).
Model Inspection and InterpretationRevise
References: sklearn/inspection
The scikitlearn library provides tools for inspecting and interpreting machine learning models, implemented in the …/inspection
directory. This directory includes functionality for computing and visualizing partial dependence plots (PDPs), calculating the permutation importance of features, and visualizing the decision boundaries of machine learning models.
Partial Dependence PlotsRevise
References: sklearn/inspection/_partial_dependence.py
, sklearn/inspection/_plot/partial_dependence.py
The scikitlearn library provides functionality for computing and visualizing partial dependence plots (PDPs) for regression and classification models. This functionality is implemented in the …/_partial_dependence.py
and …/partial_dependence.py
files.
Permutation ImportanceRevise
References: sklearn/inspection/_permutation_importance.py
The _permutation_importance.py
file in the scikitlearn
library provides functionality for computing the permutation importance of features in a trained estimator. Permutation importance is a technique for evaluating the importance of individual features by measuring the decrease in a model's performance when a feature is randomly shuffled.
Decision Boundary VisualizationRevise
References: sklearn/inspection/_plot/decision_boundary.py
The scikitlearn/sklearn/inspection/_plot/decision_boundary.py
file provides functionality for visualizing the decision boundaries of machine learning models. The main entry point is the DecisionBoundaryDisplay.from_estimator()
class method, which allows creating a DecisionBoundaryDisplay
object directly from a fitted estimator.
Utility FunctionsRevise
References: sklearn/inspection/_pd_utils.py
The important functionality in the file …/_pd_utils.py
is as follows:
Handling Missing DataRevise
References: sklearn/impute
The scikitlearn library includes various imputation methods for handling missing values in datasets, implemented in the …/impute
directory. The main components in this directory are:
Simple ImputationRevise
References: sklearn/impute/_base.py
The SimpleImputer
class in the scikitlearn/sklearn/impute/_base.py
file provides a simple and efficient way to handle missing values in data using common imputation strategies. The SimpleImputer
class is a concrete implementation of a univariate imputer that replaces missing values using strategies like mean, median, or most frequent value.
Iterative ImputationRevise
References: sklearn/impute/_iterative.py
The IterativeImputer
class in the …/_iterative.py
file provides a multivariate imputation approach for handling missing values in a dataset. The key functionality of this class is to iteratively impute the missing values by estimating each feature from all the others in a roundrobin fashion.
kNearest Neighbors ImputationRevise
References: sklearn/impute/_knn.py
The KNNImputer
class in the scikitlearn/sklearn/impute/_knn.py
file provides a way to impute missing values in a dataset using a kNearest Neighbors (kNN) based approach. The KNNImputer
class inherits from the _BaseImputer
class and offers several parameters to control the behavior of the imputation process.
Utility FunctionsRevise
References: sklearn/utils
The scikitlearn library includes a variety of utility functions and classes that are used throughout the library, implemented in the …/utils
directory.
Utility FunctionsRevise
References: sklearn/utils
The …/utils
directory contains a wide range of utility functions and classes that are used throughout the scikitlearn library. This directory provides functionality for data manipulation and validation, parallel processing, hashing, and various other tasks that are common in machine learning applications.
Estimator UtilitiesRevise
The _pprint.py
file in the scikitlearn utils
module contains the _EstimatorPrettyPrinter
class, which is used to provide custom printing functionality for estimator objects in the BaseEstimator.__repr__
method. This class extends the builtin pprint.PrettyPrinter
class and overrides several methods to handle the printing of estimators, their parameters, and related data structures.
Testing UtilitiesRevise
References: sklearn/utils/_testing.py
The assert_allclose()
and assert_allclose_dense_sparse()
functions in the …/_testing.py
file are utility functions used for testing in the scikitlearn library.
Class and Sample WeightingRevise
References: sklearn/utils/class_weight.py
The sklearn.utils.class_weight
module in scikitlearn provides utility functions for handling class weights and sample weights for unbalanced datasets. The two main functions in this module are compute_class_weight()
and compute_sample_weight()
.
Deprecation HandlingRevise
References: sklearn/utils/deprecation.py
The deprecated
decorator class, located in the …/deprecation.py
file, is a utility provided by the scikitlearn library to handle deprecation of functions and classes. This decorator serves two main purposes:
Object DiscoveryRevise
References: sklearn/utils/discovery.py
The sklearn.utils.discovery
module provides utility functions for discovering various types of objects within the scikitlearn package, including estimators, displays, and functions.
Mathematical and Data Manipulation UtilitiesRevise
References: sklearn/utils/extmath.py
The …/extmath.py
file provides a variety of mathematical and data manipulation utility functions that are used throughout the scikitlearn library.
Example Scripts and NotebooksRevise
References: examples
The scikitlearn library includes a directory with example scripts and Jupyter notebooks that demonstrate the usage of various features and functionalities. These examples cover a wide range of topics, including:
Linear ModelsRevise
References: examples/linear_model
The …/linear_model
directory contains a collection of example scripts that demonstrate the usage of various linear models and regression techniques from the scikitlearn library. The examples cover a wide range of functionality, including:
ClusteringRevise
References: examples/cluster
The …/
directory contains a collection of Python scripts that demonstrate the usage of various clustering algorithms from the scikitlearn library. The examples cover a wide range of clustering techniques, including:
Ensemble MethodsRevise
References: examples/ensemble
The …/ensemble
directory contains a collection of example scripts that demonstrate the usage and functionality of various ensemble methods in the scikitlearn library. The examples cover a wide range of ensemble techniques, including:
Model Selection and EvaluationRevise
References: examples/model_selection
The …/model_selection
directory contains a collection of Python scripts that demonstrate various aspects of model selection and evaluation in the scikitlearn library.
Support Vector MachinesRevise
References: examples/svm
The file …/plot_custom_kernel.py
demonstrates how to use a custom kernel with a Support Vector Machine (SVM) classifier to perform a 3class classification task on the Iris dataset. The key aspects of the implementation are:
Nearest NeighborsRevise
References: examples/neighbors
The …/neighbors
directory contains a collection of example files that demonstrate the usage of the sklearn.neighbors
module in the scikitlearn library. This module provides various nearest neighborsbased methods, such as kNearest Neighbors (kNN) classification and regression, Kernel Density Estimation (KDE), and Neighborhood Components Analysis (NCA).
ApplicationsRevise
References: examples/applications
The …/applications
directory contains a collection of example scripts that demonstrate the application of various machine learning techniques to realworld problems and datasets. These examples cover a wide range of topics, including:
Dimensionality ReductionRevise
References: examples/decomposition
The …/decomposition
directory contains a collection of example scripts that demonstrate the usage of various dimensionality reduction and matrix decomposition techniques from the sklearn.decomposition
module in the scikitlearn library.
Gaussian ProcessesRevise
References: examples/gaussian_process
The …/
directory contains a set of example files that demonstrate the usage of the sklearn.gaussian_process
module in the scikitlearn library. This module provides functionality for Gaussian Process Regression (GPR) and Gaussian Process Classification (GPC), which are powerful tools for regression and classification tasks.
Data PreprocessingRevise
References: examples/preprocessing
The …/preprocessing
directory contains a set of example scripts that demonstrate various data preprocessing techniques from the sklearn.preprocessing
module in the scikitlearn library. These examples cover a wide range of preprocessing tasks, including feature scaling, discretization, target encoding, and mapping data to a normal distribution.
Feature SelectionRevise
References: examples/feature_selection
The …/feature_selection
directory contains a set of example files that demonstrate various feature selection techniques available in the scikitlearn library. The examples cover topics such as:
Model Inspection and InterpretationRevise
References: examples/inspection
The …/inspection
directory contains a set of example files that demonstrate the usage of the sklearn.inspection
module in the scikitlearn library. This module provides tools for model inspection and interpretation, which are crucial for understanding the behavior and performance of machine learning models.
Neural NetworksRevise
References: examples/neural_networks
The …/neural_networks
directory contains several example scripts that demonstrate the usage of the sklearn.neural_network
module in the scikitlearn library. These examples cover various aspects of neural network models, including:
DatasetsRevise
References: examples/datasets
The …/datasets
directory contains several example scripts that demonstrate the usage of the sklearn.datasets
module in the scikitlearn library. This module provides access to various datasets that can be used for machine learning tasks.
Text ProcessingRevise
References: examples/text
The …/text
directory contains several example scripts that demonstrate the usage of text processing techniques in the scikitlearn library.
Handling Missing DataRevise
References: examples/impute
The scikitlearn library includes various imputation methods for handling missing values in datasets, implemented in the …/impute
directory. The examples in the …/impute
directory demonstrate the usage of these imputation techniques.
MultiOutput ProblemsRevise
References: examples/multioutput
The …/multioutput
directory contains an example demonstrating the usage of the sklearn.multioutput
module in the scikitlearn library. The sklearn.multioutput
module is used for handling multiple output problems, where a single model is trained to predict multiple target variables simultaneously.
Kernel ApproximationRevise
References: examples/kernel_approximation
The scikitlearn/examples/kernel_approximation
directory contains an example that demonstrates the use of the PolynomialCountSketch
class from the sklearn.kernel_approximation
module. This class is used to efficiently generate an approximation of the polynomial kernel feature space, which can then be used to train a linear classifier that approximates the accuracy of a kernelized classifier.
Developing Custom EstimatorsRevise
References: examples/developing_estimators
The scikitlearn/examples/developing_estimators
directory contains examples and guidance on developing custom estimators for the scikitlearn library. Estimators are the core components of machine learning models in scikitlearn, and the ability to create custom estimators is an important feature of the library.
Miscellaneous ExamplesRevise
References: examples/miscellaneous
The …/miscellaneous
directory contains a collection of example scripts and notebooks that demonstrate various features and functionalities of the scikitlearn library. The examples cover a wide range of topics, including anomaly detection, kernel approximation, multilabel classification, outlier detection, and more.