Run
10437916

Run 10437916

Task 5514 (Supervised Regression) bodyfat Uploaded 28-03-2020 by George Volkov
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Flow

sklearn.linear_model._ridge.Ridge(1)Linear least squares with l2 regularization. Minimizes the objective function:: ||y - Xw||^2_2 + alpha * ||w||^2_2 This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape (n_samples, n_targets)).
sklearn.linear_model._ridge.Ridge(1)_alpha1.25
sklearn.linear_model._ridge.Ridge(1)_copy_Xtrue
sklearn.linear_model._ridge.Ridge(1)_fit_intercepttrue
sklearn.linear_model._ridge.Ridge(1)_max_iternull
sklearn.linear_model._ridge.Ridge(1)_normalizefalse
sklearn.linear_model._ridge.Ridge(1)_random_state9939
sklearn.linear_model._ridge.Ridge(1)_solver"auto"
sklearn.linear_model._ridge.Ridge(1)_tol0.001

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

7 Evaluation measures

3.6081 ± 0.6447
Cross-validation details (10-fold Crossvalidation)
6.8603 ± 0.7267
Cross-validation details (10-fold Crossvalidation)
252
Cross-validation details (10-fold Crossvalidation)
0.5259 ± 0.0854
Cross-validation details (10-fold Crossvalidation)
8.3521 ± 0.9478
Cross-validation details (10-fold Crossvalidation)
4.4103 ± 0.7459
Cross-validation details (10-fold Crossvalidation)
0.528 ± 0.0734
Cross-validation details (10-fold Crossvalidation)