scikit learn - Learn Data Science with Travis

scikit-learn

Scikit-learn is a powerful and widely-used open-source machine learning library for Python. It provides a range of tools for data analysis and modeling, making it accessible for both beginners and experienced practitioners. With a user-friendly interface, scikit-learn supports various machine learning tasks, including classification, regression, clustering, and dimensionality reduction. The library also includes numerous datasets, both real-world and generated, which facilitate experimentation and learning. Its extensive documentation and active community contribute to its popularity, making scikit-learn an essential resource for anyone looking to implement machine learning solutions in Python.

5. Visualizations

Scikit-learn User Guide

Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. We provi......

7.2. Real world datasets

Scikit-learn User Guide

scikit-learn provides tools to load larger datasets, downloading them if necessary. They can be loaded using the following functions: The Olivetti faces dataset: This dataset contains a set of face......

7.1. Toy datasets

Scikit-learn User Guide

scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: These datasets are usefu......

7.4. Loading other datasets

Scikit-learn User Guide

Sample images: Scikit-learn also embeds a couple of sample JPEG images published under Creative Commons license by their authors. Those images can be useful to test algorithms and pipelines on 2D d......

7.3. Generated datasets

Scikit-learn User Guide

In addition, scikit-learn includes various random sample generators that can be used to build artificial datasets of controlled size and complexity. Generators for classification and clustering: Th......

9. Model persistence

Scikit-learn User Guide

After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following sections give you some hints on how to persist a scik......

9. Model persistence

Scikit-learn User Guide

1.14. Semi-supervised learning

Scikit-learn User Guide

Semi-supervised learning is a situation in which in your training data some of the samples are not labeled. The semi-supervised estimators in sklearn.semi_supervised are able to make use of this ad......

6.2. Feature extraction

Scikit-learn User Guide

The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. Loading featur......

8.1. Strategies to scale computationally: bigger data

Scikit-learn User Guide

For some applications the amount of examples, features (or both) and/or the speed at which they need to be processed are challenging for traditional approaches. In these cases scikit-learn has a nu......

1.4. Support Vector Machines

Scikit-learn User Guide

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high ......

10. Common pitfalls and recommended practices

Scikit-learn User Guide

The purpose of this chapter is to illustrate some common pitfalls and anti-patterns that occur when using scikit-learn. It provides examples of what not to do, along with a corresponding correct ex......