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Explicit feature map approximation for RBF kern...
An example illustrating the approximation of the feature map of an RBF kernel. It shows how to use RBFSampler and Nystroem to approximate the feature map of an RBF kernel for classification with an...scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_approximation.html -
Effect of model regularization on training and ...
In this example, we evaluate the impact of the regularization parameter in a linear model called ElasticNet. To carry out this evaluation, we use a validation curve using ValidationCurveDisplay. Th...scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.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 -
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 -
2.6. Covariance estimation — scikit-learn 1.7.1...
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 -
7.6. Random Projection — scikit-learn 1.7.1 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 -
Pipelining: chaining a PCA and a logistic regre...
The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to set the dimensionality of the PCA, Total running time of the scrip...scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html -
1.7. Gaussian Processes — scikit-learn 1.7.1 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 -
Geospatial analysis | Elastic Docs
Find documents that intersect with, are within,...efficient index searching for documents that intersect with, are within,...www.elastic.co/docs/explore-analyze/geospatial-analysis -
Manage data | Elastic Docs
is a collection of documents uniquely identified by a name...name or an alias. These documents go through a process called...www.elastic.co/docs/manage-data