Boosting and matrix factorization

A system, comprising:

• obtaining, a training dataset comprising a plurality of training samples, wherein each training sample includes feature variables and one or more output variables;
• generating, using the training dataset, a first matrix that is a sparse representation of the training dataset wherein generating the first matrix comprises:
• generating a categorical representation of the feature variables based on each numerical feature variable among the feature variables;
• generating an encoded representation of each categorical feature variable among the feature variables by encoding each categorical feature variable;
• factorizing a matrix representation of the training dataset to generate one or more matrices including a second matrix;
• generating a third matrix using (i) the second matrix and (ii) a regularization term;
• generating a fourth matrix based on (i) one or more matrices and (ii) the third matrix;
• representing, each feature of the first matrix using a vector that includes a multiple adjustable parameters;
• and
• adjusting values of the adjustable parameters using a combination of (i) a loss function, (ii) the fourth matrix, and (iii) the first matrix,
• wherein adjusting the values of the adjustable parameters comprises iteratively generating sequential models to predict a residue of the loss function until a residue of the loss function can no longer be reduced, a measure of model quality meets a quality threshold, or a size of the model has reached a maximum model size threshold.

A non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising:

• obtaining, a training dataset comprising a plurality of training samples, wherein each training sample includes feature variables and one or more output variables;
• generating, using the training dataset, a first matrix that is a sparse representation of the training dataset wherein generating the first matrix comprises:
• generating a categorical representation of the feature variables based on each numerical feature variable among the feature variables;
• generating an encoded representation of each categorical feature variable among the feature variables by encoding each categorical feature variable;
• factorizing a matrix representation of the training dataset to generate one or more matrices including a second matrix;
• generating a third matrix using (i) the second matrix and (ii) a regularization term;
• generating a fourth matrix based on (i) one or more matrices and (ii) the third matrix;
• representing, each feature of the first matrix using a vector that includes a multiple adjustable parameters;
• and
• adjusting values of the adjustable parameters using a combination of (i) a loss function, (ii) the fourth matrix, and (iii) the first matrix,
• wherein adjusting the values of the adjustable parameters comprises iteratively generating sequential models to predict a residue of the loss function until a residue of the loss function can no longer be reduced, a measure of model quality meets a quality threshold, or a size of the model has reached a maximum model size threshold.