Techniques for predicting a number of links an email campaign recipient will open are described. Elements in a dataset related to an email campaign are modeled using a tree structure, where nodes of the tree represent features of each element. A mean squared error is computed of an outcome for each of the elements to determine a weight for each respective tree. The weights are then regularized by applying a penalty, such as an elastic net penalty, to each of the weights. Then, the weights are applied to each of the trees. A weighted average of all of the outcomes of the trees is calculated, where the weighted average represents a prediction of an outcome resulting from a set of feature values. The feature values correspond to the nodes of each of the trees.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method implemented by one or more modules of a computing device for generating predictions based on a set of data, the method comprising: generating, by a data configuration module, a data matrix from the set of data, the data matrix having m rows by n columns, wherein rows of the data matrix represent individual elements and columns of the data matrix represent features of each respective element, at least one of the features corresponding to an outcome of the respective element; generating, by the data configuration module, decision trees for the data set, wherein each decision tree comprises nodes corresponding to the elements; computing, by a weight calculation module, a mean squared error of each of the outcomes to determine a weight for each respective decision tree; regularizing, by the weight calculation module, each of the weights by applying a localized elastic net penalty to each of the weights; applying, by the weight calculation module, the regularized weights to each of the decision trees; and calculating, by an average outcome determination module, a weighted average of all of the outcomes of the decision trees combined, the weighted average representing a prediction of an outcome resulting from a set of feature values that are represented by the features in the n columns of the matrix. 2. The method of claim 1, wherein the set of data is used with a model that comprises an ensemble of classification and regression trees in a random forest model. 3. The method of claim 1, wherein the mean squared error is a value found by subtracting an outcome predicted by the model from an actual outcome for each respective individual element or user. 4. The method of claim 1, wherein applying the localized elastic net penalty comprises adding the localized elastic net penalty to each of the weights. 5. The method of claim 1, wherein the individual elements correspond to email recipients, and wherein the features of each respective element comprise features of the email recipients. 6. The method of claim 5, wherein the prediction of the outcome corresponds to a prediction of a number of links a recipient of an email will click on after opening the email. 7. The method of claim 1, wherein the set of data comprises numeric and non-numeric data. 8. The method of claim 1, wherein the set of data is a training set of data that is part of a larger set of data, and the method further comprises: using additional test data from the larger set of data to test an amount of error in the prediction of the outcome, wherein the additional test data is not part of the training set of data. 9. A system comprising: one or more processors; and one or more computer-readable media configured to store instructions which, when executed by the one or more processors, perform operations to generate predictions, the operations comprising: receiving a dataset via a communication module, the dataset comprising multiple elements, each of the elements having multiple features; generating, by a data configuration module, decision trees for training data models, each decision tree having nodes corresponding to the features of the elements; computing, by a weight calculation module, a weight for each of the decision trees; applying, by the weight calculation module, a penalty to each of the weights; regularizing, by the weight calculation module, each of the weights to prevent overfitting and to reduce incorporating specialized features of data that are not generalizable to other data sets; and calculating, by an average outcome determination module, a weighted average of one or more outcomes of all of the decision trees combined to provide a prediction for an unknown element having a set of multiple features. 10. The system of claim 9, wherein the decision trees form a random forest model of decision trees, and wherein each decision tree in the random forest of trees comprises classification and regression (CART) trees. 11. The system of claim 9, wherein the penalty is a localized elastic net penalty that is added to each of the weights. 12. The system of claim 9, wherein computing the weights for each of the decision trees comprises computing an error for each of the elements. 13. The system of claim 9, wherein the dataset comprises numeric and non-numeric data. 14. The system of claim 9, the operations further comprising generating a data matrix from the dataset, wherein the data matrix includes m rows by n columns, wherein each row of the data matrix represents individual elements of the multiple elements, and each column of the data matrix represent features of each respective element. 15. A computer-implemented method of predicting a number of links a recipient of an email will open, the method comprising: creating, by a data configuration module, a data matrix comprising multiple users and features of each respective user, where each of the multiple users are distinct users; generating, by the data configuration module, models for each of the multiple users, each model having at least one outcome; computing, by a weight calculation module, weights for each of the multiple models by minimizing a loss function for the users; regularizing, by the weight calculation module, the weights for each of the multiple models by applying a penalty to each of the weights; and determining, by an average outcome determination module, a weighted average of the outcomes of all of the models, the weighted average corresponding to a prediction of the number of links the recipient of the email will open. 16. The computer-implemented method of claim 15, wherein each of the models is represented by a classification and regression tree as part of a random forest model. 17. The computer-implemented method of claim 15, wherein minimizing the loss function for each of the multiple users comprises calculating an error for each of the users by subtracting an outcome predicted by a model of the data matrix from an actual outcome for each respective user. 18. The computer-implemented method of claim 15, wherein the penalty is a localized elastic net penalty function that is imposed on each of the weights. 19. The computer-implemented method of claim 15, wherein the data matrix comprises numeric and non-numeric data. 20. The computer-implemented method of claim 15, wherein the data matrix is of m rows by n columns, wherein each row of the data matrix represents individual users of the multiple users, and each column of the data matrix represent features of each respective user.