Abstract: Techniques for quantifying accuracy of a prediction model that has been trained on a data set parameterized by multiple features are provided. The model performs in accordance with a theoretical performance manifold over an intractable input space in connection with the features. A determination is made as to which of the features are strongly correlated with performance of the model. Based on the features determined to be strongly correlated with performance of the model, parameterized sub-models are created such that, in aggregate, they approximate the intractable input space. Prototype exemplars are generated for each of the created sub-models, with the prototype exemplars for each created sub-model being objects to which the model can be applied to result in a match with the respective sub-model. The accuracy of the model is quantified using the generated prototype exemplars. A recommendation engine is provided for when there are particular areas of interest.

Abstract: Techniques for recommending a prediction model from among a number of different prediction models are provided. Each of these prediction models has been trained based on a respective training data set, and each performs in accordance with a respective theoretical performance manifold. An indication of a region definable in relation to the theoretical performance manifolds of the different prediction models is received as input. For each of the different prediction models, the indication of the region is linked to features parameterizing the respective performance manifold; and one or more portions of the respective performance manifold is/are identified based on the features determined by the linking, the portion(s) having a volume and a shape that collectively denote an expected performance of the respective model for the input. The expected performance of the prediction models for the input is compared. Based on the comparison, one or more of the models is/are suggested.

Abstract: Techniques for quantifying accuracy of a prediction model that has been trained on a data set parameterized by multiple features are provided. The model performs in accordance with a theoretical performance manifold over an intractable input space in connection with the features. A determination is made as to which of the features are strongly correlated with performance of the model. Based on the features determined to be strongly correlated with performance of the model, parameterized sub-models are created such that, in aggregate, they approximate the intractable input space. Prototype exemplars are generated for each of the created sub-models, with the prototype exemplars for each created sub-model being objects to which the model can be applied to result in a match with the respective sub-model. The accuracy of the model is quantified using the generated prototype exemplars. A recommendation engine is provided for when there are particular areas of interest.

Abstract: Techniques for recommending a prediction model from among a number of different prediction models are provided. Each of these prediction models has been trained based on a respective training data set, and each performs in accordance with a respective theoretical performance manifold. An indication of a region definable in relation to the theoretical performance manifolds of the different prediction models is received as input. For each of the different prediction models, the indication of the region is linked to features parameterizing the respective performance manifold; and one or more portions of the respective performance manifold is/are identified based on the features determined by the linking, the portion(s) having a volume and a shape that collectively denote an expected performance of the respective model for the input. The expected performance of the prediction models for the input is compared. Based on the comparison, one or more of the models is/are suggested.

Abstract: Techniques for recommending a prediction model from among a number of different prediction models are provided. Each of these prediction models has been trained based on a respective training data set, and each performs in accordance with a respective theoretical performance manifold. An indication of a region definable in relation to the theoretical performance manifolds of the different prediction models is received as input. For each of the different prediction models, the indication of the region is linked to features parameterizing the respective performance manifold; and one or more portions of the respective performance manifold is/are identified based on the features determined by the linking, the portion(s) having a volume and a shape that collectively denote an expected performance of the respective model for the input. The expected performance of the prediction models for the input is compared. Based on the comparison, one or more of the models is/are suggested.

Abstract: Techniques for quantifying accuracy of a prediction model that has been trained on a data set parameterized by multiple features are provided. The model performs in accordance with a theoretical performance manifold over an intractable input space in connection with the features. A determination is made as to which of the features are strongly correlated with performance of the model. Based on the features determined to be strongly correlated with performance of the model, parameterized sub-models are created such that, in aggregate, they approximate the intractable input space. Prototype exemplars are generated for each of the created sub-models, with the prototype exemplars for each created sub-model being objects to which the model can be applied to result in a match with the respective sub-model. The accuracy of the model is quantified using the generated prototype exemplars. A recommendation engine is provided for when there are particular areas of interest.