scikit-learn: machine learning in Python — scikit-learn 1.7.2 …
Preprocessing Feature extraction and normalization. Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature …
Getting Started — scikit-learn 1.7.2 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, …
User Guide — scikit-learn 1.7.2 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 …
1. Supervised learning — scikit-learn 1.7.2 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 …
13. Choosing the right estimator — scikit-learn 1.7.2 documentation
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 1.7.2 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 …
3.1. Cross-validation: evaluating estimator performance — scikit …
While i.i.d. data is a common assumption in machine learning theory, it rarely holds in practice. If one knows that the samples have been generated using a time-dependent process, it is safer …
Pipeline — scikit-learn 1.7.2 documentation
To be compatible with the scikit-learn API, all steps must define fit. All non-last steps must also define transform. See Combining Estimators for more details. transform_inputlist of str, …
1.10. Decision Trees — scikit-learn 1.7.2 documentation
For instance, in the example below, decision trees learn from data to approximate a sine curve with a set of if-then-else decision rules. The deeper the tree, the more complex the decision …
2. Unsupervised learning — scikit-learn 1.7.2 documentation
Feb 2, 2010 · Gaussian mixture models- Gaussian Mixture, Variational Bayesian Gaussian Mixture., Manifold learning- Introduction, Isomap, Locally Linear Embedding, Modified Locally …