scikit-learn: machine learning in Python — scikit-learn 1.7.2 …
scikit-learn Machine Learning in Python Getting Started Release Highlights for 1.7
Installing scikit-learn — scikit-learn 1.7.2 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 …
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 · 9. Computing with scikit-learn 9.1. Strategies to scale computationally: bigger data 9.1.1. Scaling with instances using out-of-core learning 9.2. Computational Performance 9.2.1. …
1. Supervised learning — scikit-learn 1.7.2 documentation
Jan 1, 2010 · Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle Regression, LARS Lasso, …
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 …
API Reference — scikit-learn 1.7.2 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 …
PCA — scikit-learn 1.7.2 documentation
PCA # class sklearn.decomposition.PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', n_oversamples=10, …
train_test_split — scikit-learn 1.7.2 documentation
sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None) [source] # Split arrays or matrices into …
LogisticRegression — scikit-learn 1.7.2 documentation
Note ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.