Added AdaBoost and KNeighbors classifiers and regressors (finally closing #7). Added support for kernel approximation samplers. (Thanks @nineil) All linear models are now supported by print_model_weights (issue #119). Added f1_score_weighted metric so that weighted F1 will be calculated even for binary classification tasks. Modified f1_score_micro and f1_score_macro to also always return average for binary classification tasks (instead of previous behavior where only performance on positive class was returned)
International audienceMost kernel-based methods, such as kernel regression, kernel PCA, ICA, or k-me...
In this work, kernelized binary support vector machines are implemented based on stochastic gradient...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
Switched from using Scipy's kernel to Scikit-learn Automatic kernel bandwidth selection Multiple-o...
Implemented enhancements Base estimators other than default are not supported for AdaBoost(#238) N...
This release adds support for scikit-learn 0.22, thereby dropping support for older versions. Moreov...
This is a somewhat major release that includes major changes as well as a number of bugfixes. Chang...
This release has some big behind-the-scenes changes. First, we split the data.py module up into a su...
The main new feature in this release is that .libsvm files are now fully supported by skll_convert a...
Update of the package to the newest scikit-learn version 0.21.2 that requires a different MRO of the...
New Features The SequentialFeatureSelector now supports using pre-specified feature sets via the fi...
We describe how Stack Filters and Weighted Order Statistic function classes can be used for classifi...
Fourth post of our series on classification from scratch, following the previous post which was some...
Rare events are involved in many challenging real world classification problems, where the minority ...
Classification metric statistics of models for different self-reported literacy profiles (Positive c...
International audienceMost kernel-based methods, such as kernel regression, kernel PCA, ICA, or k-me...
In this work, kernelized binary support vector machines are implemented based on stochastic gradient...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
Switched from using Scipy's kernel to Scikit-learn Automatic kernel bandwidth selection Multiple-o...
Implemented enhancements Base estimators other than default are not supported for AdaBoost(#238) N...
This release adds support for scikit-learn 0.22, thereby dropping support for older versions. Moreov...
This is a somewhat major release that includes major changes as well as a number of bugfixes. Chang...
This release has some big behind-the-scenes changes. First, we split the data.py module up into a su...
The main new feature in this release is that .libsvm files are now fully supported by skll_convert a...
Update of the package to the newest scikit-learn version 0.21.2 that requires a different MRO of the...
New Features The SequentialFeatureSelector now supports using pre-specified feature sets via the fi...
We describe how Stack Filters and Weighted Order Statistic function classes can be used for classifi...
Fourth post of our series on classification from scratch, following the previous post which was some...
Rare events are involved in many challenging real world classification problems, where the minority ...
Classification metric statistics of models for different self-reported literacy profiles (Positive c...
International audienceMost kernel-based methods, such as kernel regression, kernel PCA, ICA, or k-me...
In this work, kernelized binary support vector machines are implemented based on stochastic gradient...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...