Abstract: The technology is directed towards receiving training data regarding a set of observations. Each observation includes a feature set, a treatment, and an outcome. A first generation of machine learning models is trained, via the training data, to predict an outcome for a feature set of a given observation. A new generation of models is generated by selecting a subset of models from the trained first generation of models based on a fitness criteria of each model to generate an intermediate layer for use in predicting a treatment. An algorithm is applied to the selected subset of models to generate the new generation of models. Transformed training data is generated using the training data and a model of the new generation of models. The transformed training data includes, for each observation, a transformed feature set comprising a representation of the feature set in a latent space of the model.

Abstract: A digital experience enhancement system includes an ensemble deep learning model that includes an estimator ensemble and a neural network. The ensemble deep learning model is trained to generate a digital experience enhancement recommendation from an enhancement request. The ensemble deep learning model receives the enhancement request, which is input to the estimator ensemble. The estimator ensemble uses various different machine learning systems to generate estimator output values. The neural network uses the estimator output values from the estimator ensemble to generate a digital experience enhancement recommendation. The digital experience generation system then uses this digital experience enhancement recommendation to enhance the digital experience.

Abstract: A digital experience enhancement system includes an ensemble deep learning model that includes an estimator ensemble and a neural network. The ensemble deep learning model is trained to generate a digital experience enhancement recommendation from an enhancement request. The ensemble deep learning model receives the enhancement request, which is input to the estimator ensemble. The estimator ensemble uses various different machine learning systems to generate estimator output values. The neural network uses the estimator output values from the estimator ensemble to generate a digital experience enhancement recommendation. The digital experience generation system then uses this digital experience enhancement recommendation to enhance the digital experience.