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 extraction, and more...
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, model …
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 Regression, …
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 tutorial form.
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 1.1.6. Multi-task …
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.
1.17. Neural network models (supervised) - scikit-learn
Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function f: R m → R o by training on a dataset, where m is the number of dimensions for input and o is the number of …
An introduction to machine learning with scikit-learn
Machine learning is about learning some properties of a data set and then testing those properties against another data set. A common practice in machine learning is to evaluate an algorithm by …
1.10. Decision Trees — scikit-learn 1.7.2 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...
3.2. Tuning the hyper-parameters of an estimator - scikit-learn
In scikit-learn they are passed as arguments to the constructor of the estimator classes. Typical examples include C, kernel and gamma for Support Vector Classifier, alpha for Lasso, etc. It is …