Naive Bayes Classification Prediction

Data is predicted according to the model trained by Naive Bayes classification or the existing model.

When creating a Naive Bayes classification prediction task, you need to set the following parameters:

• Prediction dataset: Required parameter. The data set to be predicted accesses connection info, including data type, connection parameter, data set name, etc. You can connect HBase data, DSF data, and local data.

• Data Query Criteria: Optional parameter; the specified data can be filtered out for corresponding analysis according to the query criteria; attribute condition and spatial query are supported. E.g. SmID<100 and BBOX(the_geom, 120,30,121,31)。

• Model saving directory: Required parameter, the saving address of the model generated in the training process.

• Mapping field of

  prediction data: Optional parameter. The field of prediction data needs to be in one-to-one correspondence with the field of training data to obtain the prediction result by using the model obtained by training. The default is null. In this case, all fields in the explanation variable array must exist in the prediction data set.

• Distance explanatory variable mapping of

  prediction data: Optional parameter. If the distance explanatory variable data set is input in the training model stage, the prediction distance explanatory variable data set must be input here, and the field must correspond.

• Result dataset: Required parameter, connection info for saving the access data of the prediction result, including data type, connection parameter, dataset name, etc.