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Elastic and Opster join forces to help users ta...
functionality described in this document remain at Elastic's sole discretion....www.elastic.co/blog/elastic-opster-join-forces -
New Zealand MPs Perform Haka To Protest Controv...
the country’s founding document between the Crown and the...digg.com/digg-vids/link/new-zealand-mps-haka-treaty-protest-video -
1.7. Gaussian Processes — scikit-learn 1.5.2 do...
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: The prediction i...scikit-learn.org/stable/modules/gaussian_process.html -
2.6. Covariance estimation — scikit-learn 1.5.2...
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 of the time, such an estimation has to ...scikit-learn.org/stable/modules/covariance.html -
6.6. Random Projection — scikit-learn 1.5.2 doc...
The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc...scikit-learn.org/stable/modules/random_projection.html -
Lasso model selection via information criteria ...
This example reproduces the example of Fig. 2 of[ZHT2007]. A LassoLarsIC estimator is fit on a diabetes dataset and the AIC and the BIC criteria are used to select the best model. References ZHT200...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html -
Recursive feature elimination with cross-valida...
A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation. Data generation: We build a classification task using 3 informative fea...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html -
Swiss Roll And Swiss-Hole Reduction — scikit-le...
This notebook seeks to compare two popular non-linear dimensionality techniques, T-distributed Stochastic Neighbor Embedding (t-SNE) and Locally Linear Embedding (LLE), on the classic Swiss Roll da...scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html -
Poisson regression and non-normal loss — scikit...
This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from 1 and compares it with a linear model fitted with the usual least squ...scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html -
Failure of Machine Learning to infer causal eff...
Machine Learning models are great for measuring statistical associations. Unfortunately, unless we’re willing to make strong assumptions about the data, those models are unable to infer causal effe...scikit-learn.org/stable/auto_examples/inspection/plot_causal_interpretation.html