Run
10436682

Run 10436682

Task 146825 (Supervised Classification) Fashion-MNIST Uploaded 28-01-2020 by Nicolas Hug
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  • openml-python Sklearn_0.23.dev0.
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Flow

sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoos tingClassifier(6)Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples >= 10 000). This estimator has native support for missing values (NaNs). During training, the tree grower learns at each split point whether samples with missing values should go to the left or right child, based on the potential gain. When predicting, samples with missing values are assigned to the left or right child consequently. If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. This implementation is inspired by `LightGBM `_. .. note:: This estimator is still **experimental** for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import ``enable_hist_gradient_boosting``:: >>> # explicit...
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_l2_regularization0.0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_learning_rate0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_bins255
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_depthnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_iter100
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_leaf_nodes31
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_min_samples_leaf20
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_n_iter_no_changenull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_random_state39478
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_scoringnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_tol1e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_warm_startfalse

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.

18 Evaluation measures

Area under ROC curve
0.9735 ± 0.0367
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8845 ± 0.0374
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.872 ± 0.0415
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8817 ± 0.0404
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0301 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8848 ± 0.0374
Cross-validation details (10-fold Crossvalidation)
Number of instances
70000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8846 ± 0.0369
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8848 ± 0.0374
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1674 ± 0.0432
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1322 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4408 ± 0.0861
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8848 ± 0.0374
Cross-validation details (10-fold Crossvalidation)