Abstract: In an embodiment, a computer-implemented method for training a decision tree using a database system, the decision tree comprising a plurality nodes, comprises, by one or more computing devices: storing in a database input data for training the decision tree, the input data comprising a plurality of feature values corresponding to a plurality of features; generating a particular node of the plurality of decision nodes by: selecting a subset of the plurality of features and a subset of the input data; using one or more queries to the database system, for each feature of the subset of the plurality of features, calculating an information gain associated with the feature based on the subset of the input data; identifying a particular feature of the subset of the plurality of features associated with the highest information gain; associating the particular node with the particular feature, wherein the particular node causes the decision tree to branch based on the particular feature.

Abstract: In an embodiment, a computer-implemented method for training a decision tree using a database system, the decision tree comprising a plurality nodes, comprises, by one or more computing devices: storing in a database input data for training the decision tree, the input data comprising a plurality of feature values corresponding to a plurality of features; generating a particular node of the plurality of decision nodes by: selecting a subset of the plurality of features and a subset of the input data; using one or more queries to the database system, for each feature of the subset of the plurality of features, calculating an information gain associated with the feature based on the subset of the input data; identifying a particular feature of the subset of the plurality of features associated with the highest information gain; associating the particular node with the particular feature, wherein the particular node causes the decision tree to branch based on the particular feature.

Abstract: In an embodiment, a computer-implemented method for efficient execution of a trained neural network using a database system, the trained neural network comprising a plurality of layers each comprising weight values and bias values and programmed at each of the layers to execute an affine transformation of an activation function and an input value, comprises: for a particular layer of the trained neural network, dividing the affine transformation input a plurality of transformation pieces; executing each of the transformation pieces to result in computed pieces and writing the computed pieces to a first database table; using one or more database queries, combining the computed pieces and applying the activation function to generate a set of output data; writing the output data to one of a plurality of different second database tables that respectively correspond to the layers; repeating the dividing, executing, combining, applying and writing for all layers of the trained neural network.