

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
# | Minimum |
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1 |
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2 |
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Ratings
4.705
G2CROWD | 4.95 based on 30 reviews |
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InfoWorld | 4.55 based on professional's opinion |
Developer
Written in
Python, Cython, C, C++
Initial Release
June 2007
License
Categories
Alternatives
Machine Learning
Data Mining
Data Analysis
Data Mining
Data Analysis