scikit-learn: machine learning in Python — scikit-learn 1.6.1 …
Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature extraction, and more...
1. Supervised learning — scikit-learn 1.6.1 documentation
Jan 1, 2010 · 1. Supervised learning. 1.1. Linear Models; 1.2. Linear and Quadratic Discriminant Analysis; 1.3. Kernel ridge regression; 1.4. Support Vector Machines; 1.5. Stochastic Gradient …
12. Choosing the right estimator — scikit-learn 1.6.1 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. …
1.10. Decision Trees — scikit-learn 1.6.1 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 …
Classifier comparison — scikit-learn 1.6.1 documentation
A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be take...
2. Unsupervised learning — scikit-learn 1.6.1 documentation
Feb 2, 2010 · 1. Supervised learning. 1.1. Linear Models; 1.2. Linear and Quadratic Discriminant Analysis; 1.3. Kernel ridge regression; 1.4. Support Vector Machines; 1.5. Stochastic Gradient …
Testimonials — scikit-learn 1.6.1 documentation
The combination of consistent APIs, thorough documentation, and top notch implementation make scikit-learn our favorite machine learning package in Python. scikit-learn makes doing …
User Guide — scikit-learn 1.6.1 documentation
Jan 1, 2010 · 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. …
Getting Started — scikit-learn 1.6.1 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, …
1.17. Neural network models (supervised) - scikit-learn
Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function \(f: R^m \rightarrow R^o\) by training on a dataset, where \(m\) is the number of dimensions for input …