Machine learningOpacityTransparencyDecision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would tr
As such honest and decent precautions and analysis are needed to ensure algorithms are equal and reasonable without discrimination. Moreover, for antibiotic decision making further ethical considerations need to be taken into account including the effect on other individuals outside of the patient being...
In which, ID3 and C4.5 belong to model algorithms that using the tree structures to classify samples, while the CART algorithm is applicable for both classification and regression [84–87]. As shown in Fig. 7, selecting certain features to distinguish the data and then divided into different ...
This estimation was done by forward and backward algorithms, which are called filtering and smoothing, respectively. In filtering, the posterior distribution of \(z_t\) given observations until t (\(x_{1:t}\)) is sequentially updated in a forward direction as $$\begin{array}{*{20}{c}}...
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predictors. A small change in the data can result in a major change in the structure of the decision tree, which can convey a different result from what users will get in a normal event. The resulting change in the outcome can be managed by machine learning algorithms, such asboostingand...
Setting up a built-in preset for deep Q-learning In Amazon SageMaker RL, a preset file configures the RL training jobs and defines the hyperparameters for the RL algorithms. The following preset, for example, implements and parameterizes a deep Q-learning agent, as described in the prev...
Algorithmic solutions to the problem of maximizing reward over a single binary-choice perceptual decision-making trial include SDT and its dynamical relation, the SPRT. In both algorithms, agents infer the world state to use as a basis for a decision by subjecting a sample of observations to a...
ML algorithms are ideally suited to integrate abundant and heterogeneous data and may be the most feasible option available in many biomedical settings [8]. Moreover, medical decision-making has become increasingly complex outpacing the capacity of the human mind and can no longer be effectively cap...
Machine learning is a series of algorithms with set objective and without being explicitly programmed. It performs well in development of prediction model and has been widely used in medical data in recent years [10]. Machine learning technology may be helpful to establish a robust prediction model...