Getting Started — scikit-learn 1.6.0 documentation
The purpose of this guide is to illustrate some of the main features that scikit-learn provides. It assumes a very basic working knowledge of machine learning practices (model fitting, predicting, cross-validation, etc.). Please refer to our installation instructions for installing scikit-learn.
scikit-learn: machine learning in Python — scikit-learn 1.6.0 …
"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]"
scikit-learn Tutorials — scikit-learn 1.4.2 documentation
An introduction to machine learning with scikit-learn. Machine learning: the problem setting; Loading an example dataset; Learning and predicting; Conventions; A tutorial on statistical-learning for scientific data processing. Statistical learning: …
An introduction to machine learning with scikit-learn
In this section, we introduce the machine learning vocabulary that we use throughout scikit-learn and give a simple learning example. Machine learning: the problem setting ¶ In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data.
User Guide — scikit-learn 1.6.0 documentation
8. Computing with scikit-learn. 8.1. Strategies to scale computationally: bigger data. 8.1.1. Scaling with instances using out-of-core learning; 8.2. Computational Performance. 8.2.1. Prediction …
1. Supervised learning — scikit-learn 1.6.0 documentation
2010年1月1日 · 8. Computing with scikit-learn. 8.1. Strategies to scale computationally: bigger data; 8.2. Computational Performance; 8.3. Parallelism, resource management, and configuration; 9. Model persistence; 10. Common pitfalls and recommended practices; 11. Dispatching. 11.1. Array API support (experimental) 12. Choosing the right estimator; 13 ...
Tutorial exercises — scikit-learn 1.6.0 documentation
Visualizing cross-validation behavior in scikit-learn; Multiclass methods. Overview of multiclass training meta-estimators; Multioutput methods. Multilabel classification using a classifier chain; Nearest Neighbors. Approximate nearest neighbors in TSNE; Caching nearest neighbors; Comparing Nearest Neighbors with and without Neighborhood ...
13. External Resources, Videos and Talks - scikit-learn
Introduction to statistical learning with scikit-learn by Gael Varoquaux at SciPy 2011. An extensive tutorial, consisting of four sessions of one hour. The tutorial covers the basics of machine learning, many algorithms and how to apply them using scikit-learn.
12. Choosing the right estimator — scikit-learn 1.6.0 documentation
12. Choosing the right estimator#. Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems.
Examples — scikit-learn 1.6.0 documentation
This is the gallery of examples that showcase how scikit-learn can be used. Some examples demonstrate the use of the API in general and some demonstrate specific applications in tutorial form. Also check out our user guide for more detailed illustrations.