Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
* Be more specific about logistic regression solver in examples
* Use early stopped SGD (faster) and plot cross-validated error for best models
* Fix LR solver in /plot_voting_probas.pyexamples/ensemble/plot_voting_probas.py
* Fix LR solver & scale data in plot_digits_classification_exercise.py
* Use saga solver in plot_logistic_l1_l2_sparsity.py
* Use LBFGS solver in plot_iris_logistic.py
* Use LBFGS in plot_logistic.py
* Use SAGA solver for Logistic Regression Path example
* Use LBFGS solver in plot_classifier_chain_yeast.py
* Use LBFGS solver in plot_rbm_logistic_classification.py
* typo
* typo
* Bump up pandas dependency to 0.17.1
* Bump up examples minimal deps to match pandas 0.17.1
* Fix figure layout for plot_digits_pipe.py
* Version numbers are not decimal numbers
* Set multinomial, no scaling to keep example simple, fix formatting of example doc
* Missing plt.tight_layout() in plot_voting_probas.py
* Missing plt.tight_layout() in plot_logistic.py
This example trains several tree based ensemble methods and uses
them to transform the data into a high dimensional, sparse space.
The trains a linear model on this new feature space. The idea is
taken from:
Practical Lessons from Predicting Clicks on Ads at Facebook Junfeng Pan,
He Xinran, Ou Jin, Tianbing XU, Bo Liu, Tao Xu, Yanxin Shi, Antoine
Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela
International Workshop on Data Mining for Online Advertising (ADKDD)
https://www.facebook.com/publications/329190253909587/
Jérémie du Boisberranger