Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictions of a trained model from near random guesses, minimizing the rate of False Positives, and other problem-specific ...
讲者: Yingbin Liang Professor at the Department of Electrical and Computer Engineering at the Ohio State University (OSU) 讲座题目:Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-...
it is also critical that the model makes good predictions within predefined subpopulations. For instance, showing that a model is fair or equitable requires evaluating the model's performance in different demographic subgroups. However, subpopulation performance metrics are typically computed using only da...
Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of
5.Unit 13 Making Predictions第十三单元 预测未来 6.Gait Detection and Sequence Preprocessing for Gait Recognition步态识别中的步态检测与序列预处理 7.Techniques for Predicting Yield of Walnut Scions核桃穗条产量预测预报方法初步研究 8.The Positive Research on the Contribution Rate of Scientific and Technolog...
, savePredictions = "final", # , returnResamp = "all" ) # preprocess by standardization within each k-fold preprocess_configuration = c("center", "scale") # select variables dataset %<>% select(target_label, features_labels) %>% na.omit ...
While machine learning has a variety of useful applications, it is particularly effective in medicine in where it can be used to provide predictions about both the diagnosis and prognosis of various diseases from clinical data sets as well as to guide optimal patient treatment1. These predictions ...
K.set_learning_phase(1) def showH5Group(self, groupObj): logging.info('group name:%s, shape:%s' % (groupObj.name, groupObj.shape)) for key in groupObj.keys(): if isinstance(groupObj[key], h5py.Group): self.showH5Group(groupObj[key]) else: self.showH5Dataset(groupObj[key]) def...
STEP (1) Specify performance metrics, including measures of model discrimination (ability to distinguish cases from controls) and calibration (how well the model’s risk predictions match observed case rates). For discrimination, we used the area under the receiver operating curve (AUC) for binary...
Econometric evidence (based on cross-sectional and time-series data) confirms the theoretical predictions, and is in line with the earlier panel data-based empirical findings of [2], who too suggests that regional (i.e., Africa, Asia and Oceania, Central and South America, the European Union...