Interpretability in Machine Learning

Interpretability in Machine Learning – We present in this paper a statistical procedure that gives the maximum accuracy on the posterior of all the possible outputs of a given model with a fixed amount of data. The procedure is illustrated using a standard dataset, namely the dataset generated with a model with a certain number of parameters. The procedure is illustrated with a model with certain number of parameters.

Most current methods treat a set of discrete observations (e.g., a model and a test) as a collection of observations. Such approaches typically assume that samples are modeled as discrete samples, which may not be the case. In this work we present a new approach for classification experiments based on Bayesian networks, where the classifier is a single distribution over observations. In addition, we present a generalization error measure that enables us to compare the resulting classifiers to a subset of the observed distributions. To the best of our knowledge, our contribution is the first one to analyze data in this manner, outperforming a state-of-the-art classification algorithm in this task.

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Interpretability in Machine Learning

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  • Predicting visual stimuli based on saliency maps

    Structural Correspondence Analysis for Semi-supervised LearningMost current methods treat a set of discrete observations (e.g., a model and a test) as a collection of observations. Such approaches typically assume that samples are modeled as discrete samples, which may not be the case. In this work we present a new approach for classification experiments based on Bayesian networks, where the classifier is a single distribution over observations. In addition, we present a generalization error measure that enables us to compare the resulting classifiers to a subset of the observed distributions. To the best of our knowledge, our contribution is the first one to analyze data in this manner, outperforming a state-of-the-art classification algorithm in this task.


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