Decision-based model generation for video delivery

A method comprising:

• receiving data that includes a plurality of features, wherein a first feature in the plurality of features is designated as a decision feature in which a prediction for a plurality of prediction values that are defined for the decision feature is generated using a data structure, wherein the decision feature selects a content delivery network in a plurality of content delivery networks for delivering content to a client device or selects a profile of a plurality of profiles for delivering the content at different bitrates;
• using a decision aware splitting process, splitting a first node of the data structure into a plurality of leaf nodes using a plurality of decision values for the first feature from the data;
• using a metric-based splitting process, performing:
• for leaf nodes in the plurality of leaf nodes, generating a metric value for a metric for features other than the first feature in the plurality of features from the data, wherein the metric value is based on selecting a respective feature for a respective leaf node;
• adding a feature for respective leaf nodes based on the metric value that is generated for features for the respective leaf nodes;
• splitting the plurality of leaf nodes using a plurality of feature values for the selected feature for respective leaf nodes, wherein feature values in the plurality of feature values are associated with edges connected to leaf nodes in the plurality of leaf nodes;
• ending the metric-based splitting process when a criterium is met based on the data structure;
• selecting a respective prediction value from the plurality of prediction values for the first feature and adding the respective prediction value to each leaf node in a last split of leaf nodes to the data structure, wherein:
• the prediction value is determined based on a combination of features associated with the leaf node and respective feature values associated with edges for the combination of features,
• each leaf node has one of the plurality of prediction values added,
• the prediction values for the leaf nodes discriminates between values of the decision feature to select one of the plurality of content delivery networks to deliver the content or one of the plurality of profiles of a bitrate,
• a first leaf node has a first prediction value added and a second leaf node has a second prediction value added, and
• the first prediction value is different from the second prediction value due to using the decision aware splitting process that splits the node in the data structure using the decision feature;
• and
• outputting the data structure based on the splitting of the first node and the splitting of the plurality of leaf nodes.

A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for:

• receiving data that includes a plurality of features, wherein a first feature in the plurality of features is designated as a decision feature in which a prediction for a plurality of prediction values that are defined for the decision feature is generated using a data structure, wherein the decision feature selects a content delivery network in a plurality of content delivery networks for delivering content to a client device or selects a profile of a plurality of profiles for delivering the content at different bitrates;
• using a decision aware splitting process, splitting a first node of the data structure into a plurality of leaf nodes using a plurality of decision values for the first feature from the data;
• using a metric-based splitting process, performing:
• for leaf nodes in the plurality of leaf nodes, generating a metric value for a metric for features other than the first feature in the plurality of features from the data, wherein the metric value is based on selecting a respective feature for a respective leaf node;
• adding a feature for respective leaf nodes based on the metric value that is generated for features for the respective leaf nodes;
• splitting the plurality of leaf nodes using a plurality of feature values for the selected feature for respective leaf nodes, wherein feature values in the plurality of feature values are associated with edges connected to leaf nodes in the plurality of leaf nodes;
• ending the metric-based splitting process when a criterium is met based on the data structure;
• selecting a respective prediction value from the plurality of prediction values for the first feature and adding the respective prediction value to each leaf node in a last split of leaf nodes to the data structure, wherein:
• the prediction value is determined based on a combination of features associated with the leaf node and respective feature values associated with edges for the combination of features,
• each leaf node has one of the plurality of prediction values added;
• the prediction values for the leaf nodes discriminates between values of the decision feature to select one of the plurality of content delivery networks to deliver the content or one of the plurality of profiles of a bitrate,
• a first leaf node has a first prediction value added and a second leaf node has a second prediction value added, and
• the first prediction value is different from the second prediction value due to using the decision aware splitting process that splits the node in the data structure using the decision feature;
• and
• outputting the data structure based on the splitting of the first node and the splitting of the plurality of leaf nodes.