Hi All,
I wanted to know can i update the existing model in splunk machine learning toolkit.
If yes, how to do it?
Hi there,
Some algorithms support an argument called "partial_fit" that allows you to update an existing model. Quoting from the docs:
"The partial_fit parameter controls whether an existing model should be incrementally updated or not (default is "false"). This allows you to update an existing model using only new data without having to retrain it on the full training data set.
Example using partial_fit:
| inputlookup iris.csv | fit GaussianNB species from * partial_fit=true into My_Incremental_Model
In the example above, if My_Incremental_Model does not exist, the model is saved to it. If My_Incremental_Model exists and was trained using GaussianNB, the command updates the existing model with the new input. If My_Incremental_Model exists but was not trained by GaussianNB, an error message will be given. If partial_fit=false or partial_fit is not specified (default is false), the model specified is created and replaces the pre-trained model if one exists."
You can search for "partial_fit" on the following page to see which algorithms support it:
http://docs.splunk.com/Documentation/MLApp/3.2.0/User/Algorithms
Hi there,
Some algorithms support an argument called "partial_fit" that allows you to update an existing model. Quoting from the docs:
"The partial_fit parameter controls whether an existing model should be incrementally updated or not (default is "false"). This allows you to update an existing model using only new data without having to retrain it on the full training data set.
Example using partial_fit:
| inputlookup iris.csv | fit GaussianNB species from * partial_fit=true into My_Incremental_Model
In the example above, if My_Incremental_Model does not exist, the model is saved to it. If My_Incremental_Model exists and was trained using GaussianNB, the command updates the existing model with the new input. If My_Incremental_Model exists but was not trained by GaussianNB, an error message will be given. If partial_fit=false or partial_fit is not specified (default is false), the model specified is created and replaces the pre-trained model if one exists."
You can search for "partial_fit" on the following page to see which algorithms support it:
http://docs.splunk.com/Documentation/MLApp/3.2.0/User/Algorithms
Thanks Aoliner,
It was really helpful , but i think it's not available for algorithm which i have used (Random Forest Regressor)
You can, of course, simply overwrite the old model with a new model using new data, but you'll have to pay the full cost of building that new model from scratch.