
scikit-learn sklearn
Complete List of Commonly Used Scikit-Learn Modules
FUNCTION  | Name  | Status  | Multi-select  | URL  | 
|---|---|---|---|---|
  | Internal utilities and helper functions.  | Not started  | ||
   | Decision tree models for classification and regression.  | Not started  | ||
  | Decision tree models for classification and regression.  | Not started  | ||
  |  Support Vector Machines for classification and regression  | Not started  | ||
  | Learning with labeled and unlabeled data.  | Not started  | ||
  | Random projections for dimensionality reduction.  | Not started  | ||
  | Preprocessing tools like scaling, normalization, and encoding.  | In progress  | ||
  | Tools for creating machine learning pipelines.  | Not started  | ||
  |  Multi-layer perceptron (MLP) for classification and regression.  | Not started  | ||
  | Nearest neighbors methods and KNN.  | Not started  | ||
  | Naive Bayes classification algorithms.  | Not started  | ||
  | Multi-output estimators for regression and classification.  | Not started  | ||
  |  Tools for cross-validation, splitting, and hyper-parameter tuning.  | Not started  | study  | |
  |  Metrics for evaluating models.  | In progress  | ||
  | Nonlinear dimensionality reduction techniques (e.g., t-SNE).  | Not started  | ||
  | Linear regression, logistic regression, and related models.  | Not started  | ||
  | Kernel ridge regression.  | Not started  | ||
  | Approximation of kernel functions.  | Not started  | ||
  | Isotonic regression.  | Not started  | ||
  | Model inspection tools likeΒ partial_dependence.  | Not started  | ||
  | Imputation of missing values.  | In progress  | ||
  |  Gaussian process regression and classification.  | Not started  | ||
  | Tools for selecting features based on importance.  | Not started  | ||
  | Feature extraction from text and images.  | Not started  | ||
  |  External libraries and utilities (e.g.,Β joblibΒ for saving models).  | Not started  | ||
  |  Experimental features (subject to change in future releases).  | Not started  | ||
  |  Error handling.  | Not started  | ||
  |  Ensemble methods like Random Forest, Gradient Boosting, etc.  | Not started  | ||
  |  Simple baseline estimators.  | Not started  | ||
  | Linear and Quadratic Discriminant Analysis.  | Not started  | ||
  | Dimensionality reduction techniques (PCA, NMF, etc.).  | Not started  | ||
  | Preloaded datasets and dataset loaders.  | Not started  | ||
  |  Partial least squares and Canonical Correlation Analysis.  | Not started  | ||
  | Covariance estimation and anomaly detection.  | Not started  | ||
  | Tools for composing pipelines and transformers.  | Not started  | ||
  | Clustering algorithms.  | Not started  | ||
  | Tools for probability calibration.  | Not started  | ||
  | Base classes and mixins for building custom estimators.  | Not started  |