------------------------------ Details of built-in Estimators ------------------------------ In this page, we will introduce the built-in estimator classes in each module. In short, they all has `fit`, `predict`, `set_params` methods. You can find more details in APIs to call them directly, or evaluate them in the `experiment` class. About Classification ^^^^^^^^^^^^^^^^^^^^ Three classical semi-supervised algorithms are implemented as the baselines of safe SSL, including Transductive Support Vector Machine (``TSVM``), Label Propagation Algorithm (``LPA``) and Co-training (``CoTraining``). About Data Quality ^^^^^^^^^^^^^^^^^^ Two algorithms called ``LEAD`` (LargE margin grAph quality juDgement) and ``SLP`` (Stochastic Label Propagation) are implemented in this package. ``LEAD`` is the first algorithm to study the quality of the graph. The basic idea is that given a set of candidate graphs, when one graph has a high quality, its predictive results may have a large margin separation. Therefore, given multiple graphs with unknown quality, one should encourage to use the graphs with a large margin, rather than the graphs with a small margin, and reduce the chances of performance degradation consequently. The proposed stacking method first regenerates a new SSL data set with the predictive results of GSSL on candidate graphs, and then formulates safe GSSL as the classical semi-supervised SVM optimization on the regenerated dataset. ``SLP`` is a lightweight label propagation method for large-scale network data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. About Model Uncertainty ^^^^^^^^^^^^^^^^^^^^^^^ We provide two safe SSL algorithms named ``S4VM`` (Safe Semi-supervised Support Vector Machine) and ``SAFER`` (SAFE semi-supervised Regression) in this package. ``S4VM`` first generates a pool of diverse large margin low-density separators, and then optimizes the label assignment for the unlabeled data in the worse case under the assumption that the ground-truth label assignment can be realized by one of the obtained low-density separators. For semi-supervised regression (SSR), ``SAFER`` tries to learn a safe prediction given a set of SSR predictions obtained in various ways. To achieve this goal, the safe semi-supervised regression problem is forumlated as a geometric projection issue. When the ground-truth label assignment is realized by a convex linear combination of base regressors, the proposal is probably safe and achieve the maximal worst-case performance gain. About Ensemble ^^^^^^^^^^^^^^ We also implement an ensemble method called ``SafetyForest`` to provide a safer prediction when given a set of training models or prediction results. ``SafetyForest`` works in a similar way as ``LEAD``. The only difference between the two is that the input of the latter needs to be the predictions of graphs, but doest not need for the former. About Wrapper ^^^^^^^^^^^^^ The wrapper helps wrapping the algorithms in third-party packages such as scikit-learn. We wrap some popular supervised learning algorithms in the wrapper as examples.