scikit-learn: machine learning in Python — scikit-learn 1.8.0 …
Preprocessing Feature extraction and normalization. Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature extraction, and more...
Getting Started — scikit-learn 1.8.0 documentation
Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model …
Examples — scikit-learn 1.8.0 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.
User Guide — scikit-learn 1.8.0 documentation
Jan 1, 2010 · Supervised learning- Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle Regression, …
Installing scikit-learn — scikit-learn 1.8.0 documentation
Install the 64-bit version of Python 3, for instance from the official website. Now create a virtual environment (venv) and install scikit-learn. Note that the virtual environment is optional but strongly …
1. Supervised learning — scikit-learn 1.8.0 documentation
Jan 1, 2010 · 1. Supervised learning # 1.1. Linear Models 1.1.1. Ordinary Least Squares 1.1.2. Ridge regression and classification 1.1.3. Lasso 1.1.4. Multi-task Lasso 1.1.5. Elastic-Net 1.1.6. Multi-task …
1.10. Decision Trees — scikit-learn 1.8.0 documentation
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning s...
API Reference — scikit-learn 1.8.0 documentation
This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the raw specifications of classes and functions may not be enough to give full guidelines …
KMeans — scikit-learn 1.8.0 documentation
See also MiniBatchKMeans Alternative online implementation that does incremental updates of the centers positions using mini-batches. For large scale learning (say n_samples > 10k) …
RandomForestClassifier — scikit-learn 1.8.0 documentation
To obtain a deterministic behaviour during fitting, random_state has to be fixed. References [1] L. Breiman, “Random Forests”, Machine Learning, 45 (1), 5-32, 2001.