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https://scikit-learn.org/stable/modules/sgd.html 1.5. Stochastic Gradient Descent — scikit-learn 1.8.0 documentation Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as... stochastic gradient descent scikit learn 8 1 5 0 documentation https://scikit-learn.org/stable/modules/naive_bayes.html 1.9. Naive Bayes — scikit-learn 1.8.0 documentation Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence... scikit learn 8 1 9 naive bayes 0 documentation https://scikit-learn.org/stable/modules/cross_validation.html 3.1. Cross-validation: evaluating estimator performance — scikit-learn 1.8.0 documentation Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the... scikit learn 8 3 1 cross validation 0 documentation evaluating https://scikit-learn.org/stable/modules/partial_dependence.html 5.1. Partial Dependence and Individual Conditional Expectation plots — scikit-learn 1.8.0... Partial dependence plots (PDP) and individual conditional expectation (ICE) plots can be used to visualize and analyze interaction between the target response... scikit learn 8 5 1 partial dependence individual https://scikit-learn.org/stable/computing/scaling_strategies.html 9.1. Strategies to scale computationally: bigger data — scikit-learn 1.8.0 documentation For some applications the amount of examples, features (or both) and/or the speed at which they need to be processed are challenging for traditional... scikit learn 8 9 1 0 documentation strategies scale https://scikit-learn.org/stable/supervised_learning.html 1. Supervised learning — scikit-learn 1.8.0 documentation Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle... scikit learn 8 supervised learning 0 documentation 1 https://scikit-learn.org/stable/modules/neural_networks_supervised.html 1.17. Neural network models (supervised) — scikit-learn 1.8.0 documentation Multi-layer Perceptron: Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function f: R^m \rightarrow R^o by training on a dataset,... scikit learn 8 1 17 neural network 0 documentation models https://scikit-learn.org/stable/modules/feature_selection.html 1.13. Feature selection — scikit-learn 1.8.0 documentation The classes in the sklearn.feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’... scikit learn 8 1 13 feature selection 0 documentation https://scikit-learn.org/stable/modules/lda_qda.html 1.2. Linear and Quadratic Discriminant Analysis — scikit-learn 1.8.0 documentation Linear Discriminant Analysis ( LinearDiscriminantAnalysis) and Quadratic Discriminant Analysis ( QuadraticDiscriminantAnalysis) are two classic classifiers,... scikit learn 8 1 2 0 documentation linear quadratic https://scikit-learn.org/stable/modules/compose.html 7.1. Pipelines and composite estimators — scikit-learn 1.8.0 documentation To build a composite estimator, transformers are usually combined with other transformers or with predictors(such as classifiers or regressors). The most... scikit learn 8 7 1 0 documentation pipelines composite https://scikit-learn.org/stable/modules/isotonic.html 1.15. Isotonic regression — scikit-learn 1.8.0 documentation The class IsotonicRegression fits a non-decreasing real function to 1-dimensional data. It solves the following problem:\min \sum_i w_i (y_i - \hat{y}_i)^2... scikit learn 8 1 15 0 documentation isotonic regression https://scikit-learn.org/stable/modules/ensemble.html 1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking — scikit-learn 1.8.0... Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness... scikit learn 8 1 11 gradient boosting ensembles random https://scikit-learn.org/stable/modules/neighbors.html 1.6. Nearest Neighbors — scikit-learn 1.8.0 documentation sklearn.neighbors provides functionality for unsupervised and supervised neighbors-based learning methods. Unsupervised nearest neighbors is the foundation of... scikit learn 8 1 6 nearest neighbors 0 documentation https://scikit-learn.org/stable/modules/svm.html 1.4. Support Vector Machines — scikit-learn 1.8.0 documentation Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support... support vector machines scikit learn 8 1 4 0 documentation https://scikit-learn.org/stable/modules/gaussian_process.html 1.7. Gaussian Processes — scikit-learn 1.8.0 documentation Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of... scikit learn 8 1 7 gaussian processes 0 documentation https://scikit-learn.org/stable/modules/semi_supervised.html 1.14. Semi-supervised learning — scikit-learn 1.8.0 documentation Semi-supervised learning is a situation in which in your training data some of the samples are not labeled. The semi-supervised estimators in... semi supervised learning 8 0 documentation 1 14 scikit https://scikit-learn.org/stable/auto_examples/cross_decomposition/plot_pcr_vs_pls.html Principal Component Regression vs Partial Least Squares Regression — scikit-learn 1.8.0... This example compares Principal Component Regression(PCR) and Partial Least Squares Regression(PLS) on a toy dataset. Our goal is to illustrate how PLS can... scikit learn 1 principal component least squares 8 0 regression https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html Multiclass Receiver Operating Characteristic (ROC) — scikit-learn 1.8.0 documentation This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers. ROC curves typically... scikit learn 1 8 0 documentation multiclass receiver operating https://scikit-learn.org/stable/user_guide User Guide — scikit-learn 1.8.0 documentation Supervised learning- Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net,... scikit learn 1 8 0 documentation user guide https://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV — scikit-learn 1.8.0... Multiple metric parameter search can be done by setting the scoring parameter to a list of metric scorer names or a dict mapping the scorer names to the scorer... scikit learn 1 8 0 demonstration multi metric https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_8_0.html Release Highlights for scikit-learn 1.8 — scikit-learn 1.8.0 documentation We are pleased to announce the release of scikit-learn 1.8! Many bug fixes and improvements were added, as well as some key new features. Below we detail the... scikit learn 1 release highlights 0 documentation 8 https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html Linear and Quadratic Discriminant Analysis with covariance ellipsoid — scikit-learn 1.8.0... This example plots the covariance ellipsoids of each class and the decision boundary learned by LinearDiscriminantAnalysis(LDA) and... scikit learn 1 8 0 linear quadratic discriminant https://scikit-learn.org/stable/inspection.html 5. Inspection — scikit-learn 1.8.0 documentation Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often... scikit learn 1 8 0 documentation 5 inspection https://scikit-learn.org/stable/ scikit-learn: machine learning in Python — scikit-learn 1.8.0 documentation learn machine learning 1 8 0 scikit python documentation https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html Understanding the decision tree structure — scikit-learn 1.8.0 documentation The decision tree structure can be analysed to gain further insight on the relation between the features and the target to predict. In this example, we show... scikit learn 1 8 0 documentation decision tree understanding structure https://scikit-learn.org/stable/about About us — scikit-learn 1.8.0 documentation History: This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started working on this... scikit learn 1 8 0 documentation us https://scikit-learn.org/stable/auto_examples/cluster/index.html Clustering — scikit-learn 1.8.0 documentation Examples concerning the sklearn.cluster module. A demo of K-Means clustering on the handwritten digits data A demo of structured Ward hierarchical clustering... scikit learn 1 8 0 documentation clustering https://scikit-learn.org/stable/related_projects.html Related Projects — scikit-learn 1.8.0 documentation Projects implementing the scikit-learn estimator API are encouraged to use the scikit-learn-contrib template which facilitates best practices for testing and... scikit learn 1 8 0 documentation related projects https://scikit-learn.org/stable/modules/feature_extraction.html 7.2. Feature extraction — scikit-learn 1.8.0 documentation The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats... scikit learn 1 8 0 documentation 7 2 feature extraction https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html SGD: Weighted samples — scikit-learn 1.8.0 documentation Plot decision function of a weighted dataset, where the size of points is proportional to its weight. Total running time of the script:(0 minutes 0.042... scikit learn 1 8 0 documentation sgd weighted samples https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html Visualizing cross-validation behavior in scikit-learn — scikit-learn 1.8.0 documentation Choosing the right cross-validation object is a crucial part of fitting a model properly. There are many ways to split data into training and test sets in... 1 8 0 cross validation scikit learn visualizing behavior https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html Two-class AdaBoost — scikit-learn 1.8.0 documentation This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two “Gaussian quantiles” clusters (see... scikit learn 1 8 0 documentation two class