A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to: determine a weighting value for each of a plurality of decision trees in a random forest model hosted on a particular device, wherein the weighting is based on entropy of the respective decision tree; receive a new decision tree over a network connection; determine a weighting value for the new decision tree based on entropy of the new decision tree; determine whether to add the new the decision tree to the random forest model based on the weighting value; and predict a classification for data generated at the particular device using the random forest model. 2. The storage medium of claim 1, wherein the weighting values for the plurality of decision trees are to be determined from a label set based on a set of input data and a set of corresponding decisions generated by the respective decision tree based on the set of input data. 3. The storage medium of claim 2, wherein the instructions, when executed, further cause the machine to cache the set of input data, and determining the weighting value for the new decision tree is to comprise: providing the set of input data to the new decision tree; generating a set of new decisions using the new decision tree based on the set of input data; and determining a new label data set based on the set of new decisions, wherein the new label data is used to determining the weighting value for the new decision tree. 4. The storage medium of claim 3, wherein the weighting values for the plurality of decision trees are determined using an optimization function, and the optimization function is to be used to determine the weighting value for the new decision tree. 5. The storage medium of claim 2, wherein the input data comprises data generated by the particular device during a first window. 6. The storage medium of claim 5, wherein the input data comprises first input data and the instructions, when executed, further cause the machine to: identify second input data generated by the particular device during a second window, wherein the second window ends subsequent to the first window; use the decision trees of the random forest model to determine a second set of decisions based on the second input data; determine a respective updated weighting value for each decision tree in the random forest model, wherein the updated weighting value is also based on entropy of the respective decision tree and further based on the second input data and second set of decisions. 7. The storage medium of claim 1, wherein the new decision tree is determined to be added to the random forest model based on the weighting value determined for the new decision tree, wherein adding the new decision tree to the random forest model comprises generation of an updated random forest model. 8. The storage medium of claim 7, wherein generation of the updated random forest model comprises replacing at least a particular one of the plurality of decision trees with the new decision tree based on the weighting value of the new decision tree being higher than a weighting value for the particular decision tree. 9. The storage medium of claim 8, wherein the random forest model has a maximum number of decision trees and the particular decision tree is replaced to satisfy the maximum number of decision trees set for the random forest model. 10. The storage medium of claim 1, wherein the instructions, when executed, further cause the machine to: train a first set of decision trees; send copies of the trained first set of decision trees to a second device in a machine-to-machine network; receive copies of a second set of decision trees from the second device; generate the random forest model to include the first and second sets of decision trees in the plurality of decision trees. 11. The storage medium of claim 10, wherein the new decision tree is received from another device in a network comprising the particular device and the other device. 12. The storage medium of claim 11, wherein the network comprises a plurality of devices, the other device is a new device within the network. 13. The storage medium of claim 1, wherein the classification comprises a prediction of whether each data instance in a set of data generated by the particular device is an anomaly. 14. The storage medium of claim 1, wherein using the random forest model predict a classification for a particular data instance generated at the particular device comprises: determining a decision for each decision tree in the random forest model; and using the decisions to determine a prediction for the particular data instance. 15. A method comprising: determining a weighting value for each of a plurality of decision trees in a random forest model hosted on a particular device, wherein the weighting is based on entropy of the respective decision tree; receiving a new decision tree over a network connection; determining a weighting value for the new decision tree based on entropy of the new decision tree; determining whether to add the new the decision tree to the random forest model based on the weighting value; and predicting a classification for data generated at the particular device using the random forest model. 16. A system comprising: a first device comprising: a processor device; memory; a communication module to communicate over a network; and decision tree model logic executable by the processor device to: determine a weighting value for each of a plurality of decision trees in a random forest model hosted in the memory of the first device, wherein the weighting is based on entropy of the respective decision tree; identify a new decision tree received over the network; determine a weighting value for the new decision tree based on entropy of the new decision tree; determine whether to add the new the decision tree to the random forest model based on the weighting value; and predict a classification for data generated at the first device using the random forest model. 17. The system of claim 16, wherein the first device further comprises one or more sensors to generate the data. 18. The system of claim 16, wherein the weighting values for the plurality of decision trees are to be determined from a label set based on a set of input data and a set of corresponding decisions generated by the respective decision tree based on the set of input data, and the set of input data is to be cached in the memory for use in determining the weighting value of the new decision tree. 19. The system of claim 16, further comprising: a second device to train a set of decision trees and send copies of the set of decision trees to the first device over the network, wherein the plurality of decision trees comprises the copies of the set of decision trees. 20. The system of claim 19, wherein the set of decision trees comprises a first set of decision trees, and the decision tree logic of the first device is further to: train a second set of decision trees; and send copies of the second set of decision trees to the second device over the network, wherein the plurality of decision trees further comprises the second set of decision trees, and the second device is to generate a random forest comprising at least the first set of decision trees and the copies of the second set of decision trees.