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Scikit Learn API

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Created

Sep 1, 2025 10:05 PM

Date

Materials

Sep 14, 2025 9:21 PM

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scikit-learn sklearn

Complete List of Commonly Used Scikit-Learn Modules

Function	Description	Status	Status	Multi-select
		No content	Not started
`sklearn.utils`	Internal utilities and helper functions.	No content	Not started
`sklearn.tree`:	Decision tree models for classification and regression.		Not started
`sklearn.tree`:	Decision tree models for classification and regression.		Not started
`sklearn.svm`:	Support Vector Machines for classification and regression		Not started
`sklearn.semi_supervised`:	Learning with labeled and unlabeled data.		Not started
`sklearn.random_projection`:	Random projections for dimensionality reduction.		Not started
`sklearn.preprocessing`:	📂 Preprocessing tools like scaling, normalization, and encoding.		In progress
`sklearn.pipeline`:	Tools for creating machine learning pipelines.		Not started
`sklearn.neural_network`:	Multi-layer perceptron (MLP) for classification and regression.		Not started
`sklearn.neighbors`	Nearest neighbors methods and KNN.		Not started
`sklearn.naive_bayes`:	Naive Bayes classification algorithms.		Not started
`sklearn.multioutput`:	Multi-output estimators for regression and classification.		Not started
`sklearn.model_selection`:	Tools for cross-validation, splitting, and hyper-parameter tuning.		Not started	study
`sklearn.metrics`:	Metrics for evaluating models.		In progress
`sklearn.manifold`:	Nonlinear dimensionality reduction techniques (e.g., t-SNE).		Not started
`sklearn.linear_model`:	Linear regression, logistic regression, and related models.		Not started
`sklearn.kernel_ridge`:	Kernel ridge regression.		Not started
`sklearn.kernel_approximation`:	Approximation of kernel functions.		Not started
`sklearn.isotonic`:	Isotonic regression.		Not started
`sklearn.inspection`:	Model inspection tools like partial_dependence.		Not started
`sklearn.impute`	📂 Imputation of missing values.		In progress
`sklearn.gaussian_process`	Gaussian process regression and classification.		Not started
`sklearn.feature_selection`	Tools for selecting features based on importance.		Not started
`sklearn.feature_extraction`	Feature extraction from text and images.		Not started
`sklearn.externals`	External libraries and utilities (e.g., joblib for saving models).		Not started
`sklearn.experimental`	Experimental features (subject to change in future releases).		Not started
`sklearn.exceptions`	Error handling.		Not started
`sklearn.ensemble`	Ensemble methods like Random Forest, Gradient Boosting, etc.		Not started
`sklearn.dummy`	Simple baseline estimators.		Not started
`sklearn.discriminant_analysis`	Linear and Quadratic Discriminant Analysis.		Not started
`sklearn.decomposition`	Dimensionality reduction techniques (PCA, NMF, etc.).		Not started
`sklearn.datasets`	Preloaded datasets and dataset loaders.		Not started
`sklearn.cross_decomposition`	Partial least squares and Canonical Correlation Analysis.		Not started
`sklearn.covariance`	Covariance estimation and anomaly detection.		Not started
`sklearn.compose`	Tools for composing pipelines and transformers.		Not started
`sklearn.cluster`	Clustering algorithms.		Not started
`sklearn.calibration`	Tools for probability calibration.		Not started
`sklearn.base`	Base classes and mixins for building custom estimators.		Not started