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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/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/dev/getting_started.html Getting Started — scikit-learn 1.9.dev0 documentation Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting,... scikit learn 1 9 dev0 documentation getting started 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/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 https://scikit-learn.org/dev/developers/tips.html Developers’ Tips and Tricks — scikit-learn 1.9.dev0 documentation Productivity and sanity-preserving tips: In this section we gather some useful advice and tools that may increase your quality-of-life when reviewing pull... scikit learn 1 9 dev0 documentation tips tricks https://scikit-learn.org/stable/model_selection.html 3. Model selection and evaluation — scikit-learn 1.8.0 documentation Cross-validation: evaluating estimator performance- Computing cross-validated metrics, Cross validation iterators, A note on shuffling, Cross validation and... scikit learn 1 8 0 documentation 3 model selection evaluation https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html A demo of structured Ward hierarchical clustering on an image of coins — scikit-learn 1.8.0... Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in order for each segmented region to be in... scikit learn 1 hierarchical clustering 8 0 demo structured https://scikit-learn.org/stable/modules/biclustering.html 2.4. Biclustering — scikit-learn 1.8.0 documentation Biclustering algorithms simultaneously cluster rows and columns of a data matrix. These clusters of rows and columns are known as biclusters. Each determines a... scikit learn 1 8 0 documentation 2 4 https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html Demonstrating the different strategies of KBinsDiscretizer — scikit-learn 1.8.0 documentation This example presents the different strategies implemented in KBinsDiscretizer: ‘uniform’: The discretization is uniform in each feature, which means that the... scikit learn 1 8 0 documentation different strategies demonstrating https://scikit-learn.org/stable/faq Frequently Asked Questions — scikit-learn 1.8.0 documentation Here we try to give some answers to questions that regularly pop up on the mailing list. Table of Contents: About the project- What is the project name (a lot... frequently asked questions scikit learn 1 8 0 documentation https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html Gradient Boosting regularization — scikit-learn 1.8.0 documentation Illustration of the effect of different regularization strategies for Gradient Boosting. The example is taken from Hastie et al 2009 1. The loss function used... scikit learn 1 8 0 documentation gradient boosting regularization https://scikit-learn.org/stable/modules/preprocessing_targets.html 7.9. Transforming the prediction target (y) — scikit-learn 1.8.0 documentation Transforming the prediction target ( y): These are transformers that are not intended to be used on features, only on supervised learning targets. See also... scikit learn 1 8 0 documentation 7 9 transforming prediction https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html Recursive feature elimination — scikit-learn 1.8.0 documentation This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten... scikit learn 1 8 0 documentation recursive feature elimination https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html Lasso model selection: AIC-BIC / cross-validation — scikit-learn 1.8.0 documentation This example focuses on model selection for Lasso models that are linear models with an L1 penalty for regression problems. Indeed, several strategies can be... scikit learn 1 8 0 documentation model selection cross validation lasso https://scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html Wikipedia principal eigenvector — scikit-learn 1.8.0 documentation A classical way to assert the relative importance of vertices in a graph is to compute the principal eigenvector of the adjacency matrix so as to assign to... scikit learn 1 8 0 documentation wikipedia principal https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_ridge_regression.html Comparison of kernel ridge regression and SVR — scikit-learn 1.8.0 documentation Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel trick, i.e., they learn a linear function in the space induced... scikit learn 1 8 0 documentation ridge regression comparison kernel https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html Plot multi-class SGD on the iris dataset — scikit-learn 1.8.0 documentation Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the... scikit learn 1 8 0 documentation multi class iris dataset plot https://scikit-learn.org/stable/auto_examples/model_selection/index.html Model Selection — scikit-learn 1.8.0 documentation Examples related to the sklearn.model_selection module. Balance model complexity and cross-validated score Class Likelihood Ratios to measure classification... scikit learn 1 8 0 documentation model selection https://scikit-learn.org/stable/visualizations.html 6. Visualizations — scikit-learn 1.8.0 documentation Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual... scikit learn 1 8 0 documentation 6 visualizations https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html Demonstration of k-means assumptions — scikit-learn 1.8.0 documentation This example is meant to illustrate situations where k-means produces unintuitive and possibly undesirable clusters. Data generation: The function make_blobs... scikit learn 1 8 0 documentation demonstration means assumptions https://scikit-learn.org/stable/auto_examples/model_selection/plot_likelihood_ratios.html Class Likelihood Ratios to measure classification performance — scikit-learn 1.8.0 documentation This example demonstrates the class_likelihood_ratios function, which computes the positive and negative likelihood ratios ( LR+, LR-) to assess the predictive... scikit learn 1 8 0 documentation class likelihood ratios https://scikit-learn.org/dev/about.html About us — scikit-learn 1.9.dev0 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 9 dev0 documentation us https://scikit-learn.org/stable/modules/preprocessing.html 7.3. Preprocessing data — scikit-learn 1.8.0 documentation The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is... scikit learn 1 8 0 documentation 7 3 preprocessing data https://scikit-learn.org/stable/testimonials/testimonials.html Testimonials — scikit-learn 1.8.0 documentation Who is using scikit-learn?: J.P.Morgan: Scikit-learn is an indispensable part of the Python machine learning toolkit at JPMorgan. It is very widely used across... scikit learn 1 8 0 documentation testimonials https://scikit-learn.org/stable/modules/clustering.html 2.3. Clustering — scikit-learn 1.8.0 documentation Clustering of unlabeled data can be performed with the module sklearn.cluster. Each clustering algorithm comes in two variants: a class, that implements the... scikit learn 1 8 0 documentation 2 3 clustering https://scikit-learn.org/stable/model_persistence.html 10. Model persistence — scikit-learn 1.8.0 documentation Summary of model persistence methods:,,, Persistence method, Pros, Risks / Cons,,, ONNX, Serve models without a Python environment, Serving and training... scikit learn 1 8 0 documentation 10 model persistence https://scikit-learn.org/stable/modules/covariance.html 2.6. Covariance estimation — scikit-learn 1.8.0 documentation Many statistical problems require the estimation of a population’s covariance matrix, which can be seen as an estimation of data set scatter plot shape. Most... scikit learn 1 8 0 documentation 2 6 covariance estimation https://scikit-learn.org/stable/datasets/loading_other_datasets.html 8.4. Loading other datasets — scikit-learn 1.8.0 documentation Sample images: Scikit-learn also embeds a couple of sample JPEG images published under Creative Commons license by their authors. Those images can be useful to... scikit learn 1 8 4 0 documentation loading datasets https://scikit-learn.org/stable/auto_examples/ensemble/plot_hgbt_regression.html Features in Histogram Gradient Boosting Trees — scikit-learn 1.8.0 documentation Histogram-Based Gradient Boosting(HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient... scikit learn 1 8 0 documentation gradient boosting features histogram https://scikit-learn.org/stable/modules/kernel_approximation.html 7.7. Kernel Approximation — scikit-learn 1.8.0 documentation This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector... scikit learn 1 8 0 documentation 7 kernel approximation https://scikit-learn.org/stable/modules/density.html 2.8. Density Estimation — scikit-learn 1.8.0 documentation Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation... scikit learn 1 2 8 density estimation 0 documentation https://scikit-learn.org/stable/modules/learning_curve.html 3.5. Validation curves: plotting scores to evaluate models — scikit-learn 1.8.0 documentation Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The bias of an estimator is... scikit learn 1 8 0 documentation 3 5 evaluate models validation https://scikit-learn.org/stable/developers/contributing.html Contributing — scikit-learn 1.8.0 documentation This project is a community effort, shaped by a large number of contributors from across the world. For more information on the history and people behind... scikit learn 1 8 0 documentation contributing https://scikit-learn.org/stable/glossary.html Glossary of Common Terms and API Elements — scikit-learn 1.8.0 documentation This glossary hopes to definitively represent the tacit and explicit conventions applied in Scikit-learn and its API, while providing a reference for users and... scikit learn 1 8 0 documentation common terms glossary api https://scikit-learn.org/stable/auto_examples/inspection/index.html Inspection — scikit-learn 1.8.0 documentation Examples related to the sklearn.inspection module. Common pitfalls in the interpretation of coefficients of linear models Failure of Machine Learning to infer... scikit learn 1 8 0 documentation inspection https://scikit-learn.org/stable/modules/permutation_importance.html 5.2. Permutation feature importance — scikit-learn 1.8.0 documentation Permutation feature importance is a model inspection technique that measures the contribution of each feature to a fitted model’s statistical performance on a... scikit learn 1 8 0 documentation 5 2 permutation feature https://scikit-learn.org/stable/api/index API Reference — scikit-learn 1.8.0 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... scikit learn 1 8 0 documentation api reference https://scikit-learn.org/dev/auto_examples/classification/plot_classifier_comparison.html Classifier comparison — scikit-learn 1.9.dev0 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... scikit learn 1 9 dev0 documentation classifier comparison https://scikit-learn.org/stable/datasets.html 8. Dataset loading utilities — scikit-learn 1.8.0 documentation The sklearn.datasets package embeds some small toy datasets and provides helpers to fetch larger datasets commonly used by the machine learning community to... scikit learn 1 0 documentation 8 dataset loading https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html SGD: Penalties — scikit-learn 1.8.0 documentation Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net. All of the above are supported by SGDClassifier and SGDRegressor.... scikit learn 1 8 0 documentation sgd penalties https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html LogisticRegression — scikit-learn 1.8.0 documentation Gallery examples: Probability Calibration curves Plot classification probability Column Transformer with Mixed Types Pipelining: chaining a PCA and a logistic... scikit learn 1 8 0 documentation https://scikit-learn.org/stable/auto_examples/index Examples — scikit-learn 1.8.0 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... scikit learn 1 8 0 documentation examples https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_decision_regions.html Visualizing the probabilistic predictions of a VotingClassifier — scikit-learn 1.8.0 documentation Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. First, three linear... scikit learn 1 8 0 documentation visualizing probabilistic predictions https://scikit-learn.org/stable/getting_started.html Getting Started — scikit-learn 1.8.0 documentation Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting,... scikit learn 1 8 0 documentation getting started https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html Segmenting the picture of greek coins in regions — scikit-learn 1.8.0 documentation This example uses Spectral clustering on a graph created from voxel-to-voxel difference on an image to break this image into multiple partly-homogeneous... scikit learn 1 8 0 documentation segmenting picture greek https://scikit-learn.org/stable/data_transforms.html 7. Dataset transformations — scikit-learn 1.8.0 documentation scikit-learn provides a library of transformers, which may clean (see Preprocessing data), reduce (see Unsupervised dimensionality reduction), expand (see... scikit learn 1 8 0 documentation 7 dataset transformations https://scikit-learn.org/stable/unsupervised_learning.html 2. Unsupervised learning — scikit-learn 1.8.0 documentation Gaussian mixture models- Gaussian Mixture, Variational Bayesian Gaussian Mixture., Manifold learning- Introduction, Isomap, Locally Linear Embedding, Modified... scikit learn 1 8 0 documentation unsupervised learning 2 https://scikit-learn.org/stable/computing/computational_performance.html 9.2. Computational Performance — scikit-learn 1.8.0 documentation For some applications the performance (mainly latency and throughput at prediction time) of estimators is crucial. It may also be of interest to consider the... scikit learn 1 8 0 documentation 9 2 computational performance https://scikit-learn.org/stable/auto_examples/text/index.html Working with text documents — scikit-learn 1.8.0 documentation Examples concerning the sklearn.feature_extraction.text module. Classification of text documents using sparse features Clustering text documents using k-means... scikit learn 1 8 0 documentation working text documents https://scikit-learn.org/stable/datasets/sample_generators.html 8.3. Generated datasets — scikit-learn 1.8.0 documentation In addition, scikit-learn includes various random sample generators that can be used to build artificial datasets of controlled size and complexity. Generators... scikit learn 1 8 3 0 documentation generated datasets https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html Faces recognition example using eigenfaces and SVMs — scikit-learn 1.8.0 documentation The dataset used in this example is a preprocessed excerpt of the “Labeled Faces in the Wild”, aka LFW:... scikit learn 1 8 0 documentation example using faces recognition https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html Gradient Boosting regression — scikit-learn 1.8.0 documentation This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for... scikit learn 1 8 0 documentation gradient boosting regression https://scikit-learn.org/stable/metadata_routing.html 4. Metadata Routing — scikit-learn 1.8.0 documentation This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or... scikit learn 1 8 0 documentation 4 metadata routing https://scikit-learn.org/stable/modules/grid_search.html 3.2. Tuning the hyper-parameters of an estimator — scikit-learn 1.8.0 documentation Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of the... scikit learn 1 8 0 documentation 3 2 tuning hyper https://scikit-learn.org/stable/modules/random_projection.html 7.6. Random Projection — scikit-learn 1.8.0 documentation The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled... scikit learn 1 8 0 documentation 7 6 random projection https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html Comparison of LDA and PCA 2D projection of Iris dataset — scikit-learn 1.8.0 documentation The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal... scikit learn 1 8 0 documentation iris dataset comparison lda https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html Visualization of MLP weights on MNIST — scikit-learn 1.8.0 documentation Sometimes looking at the learned coefficients of a neural network can provide insight into the learning behavior. For example if weights look unstructured,... scikit learn 1 8 0 documentation visualization mlp weights https://scikit-learn.org/stable/modules/impute.html 7.4. Imputation of missing values — scikit-learn 1.8.0 documentation For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are... scikit learn 1 8 0 documentation 7 4 missing values imputation https://scikit-learn.org/stable/modules/classification_threshold.html 3.3. Tuning the decision threshold for class prediction — scikit-learn 1.8.0 documentation Classification is best divided into two parts: the statistical problem of learning a model to predict, ideally, class probabilities;, the decision problem to... scikit learn 1 8 0 documentation 3 tuning decision https://scikit-learn.org/stable/modules/decomposition.html 2.5. Decomposing signals in components (matrix factorization problems) — scikit-learn 1.8.0... Principal component analysis (PCA): Exact PCA and probabilistic interpretation: PCA is used to decompose a multivariate dataset in a set of successive... scikit learn 1 2 5 8 0 decomposing signals https://scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html Concatenating multiple feature extraction methods — scikit-learn 1.8.0 documentation In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good... scikit learn 1 8 0 documentation feature extraction concatenating multiple https://scikit-learn.org/dev/whats_new.html Release History — scikit-learn 1.9.dev0 documentation Changelogs and release notes for all scikit-learn releases are linked in this page. Version 1.9- Version 1.9.dev0., Version 1.8- Version 1.8.0., Version 1.7-... scikit learn 1 9 dev0 documentation release history https://scikit-learn.org/stable/roadmap.html Roadmap — scikit-learn 1.8.0 documentation Purpose of this document: This document lists general directions that core contributors are interested to see developed in scikit-learn. The fact that an item... scikit learn 1 8 0 documentation roadmap https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge.html Ordinary Least Squares and Ridge Regression — scikit-learn 1.8.0 documentation Ordinary Least Squares: We illustrate how to use the ordinary least squares (OLS) model, LinearRegression, on a single feature of the diabetes dataset. We... scikit learn 1 8 0 documentation least squares ridge regression ordinary https://scikit-learn.org/stable/modules/manifold.html 2.2. Manifold learning — scikit-learn 1.8.0 documentation Look for the bare necessities, The simple bare necessities, Forget about your worries and your strife, I mean the bare necessities, Old Mother Nature’s... scikit learn 1 8 0 documentation 2 manifold learning https://scikit-learn.org/stable/modules/metrics.html 7.8. Pairwise metrics, Affinities and Kernels — scikit-learn 1.8.0 documentation The sklearn.metrics.pairwise submodule implements utilities to evaluate pairwise distances or affinity of sets of samples. This module contains both distance... scikit learn 1 7 8 0 documentation pairwise metrics https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html Release Highlights for scikit-learn 1.0 — scikit-learn 1.8.0 documentation We are very pleased to announce the release of scikit-learn 1.0! The library has been stable for quite some time, releasing version 1.0 is recognizing that and... scikit learn 1 release highlights 8 documentation 0 https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_multilabel.html Multilabel classification — scikit-learn 1.8.0 documentation This example simulates a multi-label document classification problem. The dataset is generated randomly based on the following process: pick the number of... scikit learn 1 8 0 documentation classification https://scikit-learn.org/dev/api/index.html API Reference — scikit-learn 1.9.dev0 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... scikit learn 1 9 dev0 documentation api reference https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html Kernel Density Estimate of Species Distributions — scikit-learn 1.8.0 documentation This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon the Haversine... scikit learn 1 8 0 documentation kernel density estimate species https://scikit-learn.org/stable/common_pitfalls.html 11. Common pitfalls and recommended practices — scikit-learn 1.8.0 documentation The purpose of this chapter is to illustrate some common pitfalls and anti-patterns that occur when using scikit-learn. It provides examples of what not to do,... scikit learn 1 8 0 documentation 11 common recommended practices pitfalls https://scikit-learn.org/stable/auto_examples/decomposition/index.html Decomposition — scikit-learn 1.8.0 documentation Examples concerning the sklearn.decomposition module. Blind source separation using FastICA Comparison of LDA and PCA 2D projection of Iris dataset Faces... scikit learn 1 8 0 documentation decomposition https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html Ridge coefficients as a function of the L2 Regularization — scikit-learn 1.8.0 documentation A model that overfits learns the training data too well, capturing both the underlying patterns and the noise in the data. However, when applied to unseen... scikit learn 1 8 0 documentation ridge coefficients function https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html Plotting Learning Curves and Checking Models’ Scalability — scikit-learn 1.8.0 documentation In this example, we show how to use the class LearningCurveDisplay to easily plot learning curves. In addition, we give an interpretation to the learning... scikit learn 1 8 0 documentation learning curves plotting checking https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html IsolationForest example — scikit-learn 1.8.0 documentation An example using IsolationForest for anomaly detection. The Isolation Forest is an ensemble of “Isolation Trees” that “isolate” observations by recursive... scikit learn 1 8 0 documentation example https://scikit-learn.org/stable/user_guide.html 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/modules/generated/sklearn.ensemble.IsolationForest.html IsolationForest — scikit-learn 1.8.0 documentation Gallery examples: IsolationForest example Comparing anomaly detection algorithms for outlier detection on toy datasets Evaluation of outlier detection... scikit learn 1 8 0 documentation https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html Principal Component Analysis (PCA) on Iris Dataset — scikit-learn 1.8.0 documentation This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on the Iris dataset. This dataset is made of 4 features:... principal component analysis scikit learn 1 8 0 documentation iris dataset pca https://scikit-learn.org/stable/computing/parallelism.html 9.3. Parallelism, resource management, and configuration — scikit-learn 1.8.0 documentation Parallelism: Some scikit-learn estimators and utilities parallelize costly operations using multiple CPU cores. Depending on the type of estimator and... scikit learn 1 8 0 documentation 9 3 resource management parallelism https://scikit-learn.org/stable/dispatching.html 12. Dispatching — scikit-learn 1.8.0 documentation Array API support (experimental)- Enabling array API support, Example usage, Support for Array API-compatible inputs, Input and output array type handling,... scikit learn 1 8 0 documentation 12 dispatching 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