scikit-learn
A SciPy based Python library for machine learning tasks like classification, regression, and clustering
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| + | Supervised Learning Algorithms | Tools for common supervised learning algorithms such as linear regression, support vector machines, and random forests; allowing you to build models for prediction tasks |
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| + | Unsupervised Learning Algorithms | Implements unsupervised learning methods like clustering, factor analysis, and principal component analysis; for exploring unlabeled data and uncovering hidden patterns |
| + | Cross-validation | Techniques to assess the predictive performance of the models, choose the best model and prevent overfitting |
| + | Preprocessing | Functions for preprocessing data, such as scaling, centring, normalization, binarization, and imputation of missing values |
| + | Model Evaluation | Metrics and scoring functions to evaluate the performance of models |
| + | Pipeline | Streamlining the machine learning workflow by chaining transformations and models |
| + | Grid Search | Methods for parameter tuning to determine the best model parameters and avoid manual exploration |
| + | Persistence | Allows saving and loading models for later use, facilitating deployment and reusability |
| + | Scalability | Supports handling large datasets through efficient algorithms and integration with tools like scikit-learn pipelines |
| + | Visualization | Offers tools for visualizing data and model performance through integration with libraries like Matplotlib |
| + | Feature Extraction | Tools for extracting features from data such as text and images for machine learning algorithms |
| + | Dimensionality Reduction | Methods like PCA and feature selection techniques to reduce the number of features |
| + | Ensemble Methods | Combines the predictions of several base estimators to improve generalizability and robustness over a single estimator |
| + | Feature Selection | Techniques for feature selection to improve estimators’ accuracy scores or to boost their performance on very high-dimensional datasets |
| + | Datasets | Provides several toy datasets to practice machine-learning techniques |
| + | Metrics | Offers a wide range of performance metrics for classification, regression, clustering, and pairwise metrics |
| + | Semi-Supervised Learning | Algorithms for semi-supervised learning problems |
| + | Nearest Neighbors | Algorithms for unsupervised and supervised neighbors-based learning methods |
| + | Gaussian Processes | Tools for Gaussian process regression and classification |
| + | Manifold Learning | Algorithms for manifold learning with an emphasis on non-linear dimensionality reduction |
| + | Covariance Estimation | Methods for robust covariance estimation and Mahalanobis distances relevance |
| + | Isotonic Regression | Implements isotonic regression to fit a non-decreasing function to data |
| + | Multiclass and Multilabel Algorithms | Strategies to solve multiclass and multilabel classification problems |
| + | Random Projection | Methods for reducing dimensionality through random projection matrix generation |
| - | Limited Deep Learning Support | Limited capabilities for deep learning tasks |
| - | High-Dimensional Data | Challenges in effectively handling high-dimensional data |
| - | Graph Algorithms | Not optimized for graph algorithms |
| - | String Processing | Not very efficient at processing strings |
| - | Hyperparameter Spaces | Awkward definition of hyperparameters and search spaces in models |
System Requirements
Version ↓
| # | Minimum |
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| 1 |
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| 2 |
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Developer
Written in
Python, Cython, C, C++
Initial Release
June 2007
License
Categories
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