- Sort Score
- Num 10 results
- Language All
- Labels All
Results 1271 - 1280 of over 10,000 for 1 (2.13 seconds)
Filter
-
Covariance estimation — scikit-learn 1.8.0 docu...
Examples concerning the sklearn.covariance module. Ledoit-Wolf vs OAS estimation Robust covariance estimation and Mahalanobis distances relevance Robust vs Empirical covariance estimate Shrinkage c...scikit-learn.org/stable/auto_examples/covariance/index.html -
Kernel Approximation — scikit-learn 1.8.0 docum...
Examples concerning the sklearn.kernel_approximation module. Scalable learning with polynomial kernel approximationscikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
sklearn.gaussian_process — scikit-learn 1.8.0 d...
Gaussian process based regression and classification. User guide. See the Gaussian Processes section for further details. Kernels: A set of kernels that can be combined by operators and used in Gau...scikit-learn.org/stable/api/sklearn.gaussian_process.html -
sklearn.impute — scikit-learn 1.8.0 documentation
Transformers for missing value imputation. User guide. See the Imputation of missing values section for further details.scikit-learn.org/stable/api/sklearn.impute.html -
sklearn.base — scikit-learn 1.8.0 documentation
Base classes for all estimators and various utility functions.scikit-learn.org/stable/api/sklearn.base.html -
sklearn.linear_model — scikit-learn 1.8.0 docum...
A variety of linear models. User guide. See the Linear Models section for further details. The following subsections are only rough guidelines: the same estimator can fall into multiple categories,...scikit-learn.org/stable/api/sklearn.linear_model.html -
sklearn.compose — scikit-learn 1.8.0 documentation
Meta-estimators for building composite models with transformers. In addition to its current contents, this module will eventually be home to refurbished versions of Pipeline and FeatureUnion. User ...scikit-learn.org/stable/api/sklearn.compose.html -
sklearn.cross_decomposition — scikit-learn 1.8....
Algorithms for cross decomposition. User guide. See the Cross decomposition section for further details.scikit-learn.org/stable/api/sklearn.cross_decomposition.html -
sklearn.discriminant_analysis — scikit-learn 1....
Linear and quadratic discriminant analysis. User guide. See the Linear and Quadratic Discriminant Analysis section for further details.scikit-learn.org/stable/api/sklearn.discriminant_analysis.html -
sklearn.semi_supervised — scikit-learn 1.8.0 do...
Semi-supervised learning algorithms. These algorithms utilize small amounts of labeled data and large amounts of unlabeled data for classification tasks. User guide. See the Semi-supervised learnin...scikit-learn.org/stable/api/sklearn.semi_supervised.html