Properties that can be configured for the HANA Naive Bayes algorithm.
Naive Bayes is a classification algorithm based on Bayes theorem. It estimates the class-conditional probability by assuming that the attributes are conditionally independent of one another. Despite its simplicity, Naive Bayes works quite well in areas like document classification and spam filtering, and it only requires a small amount of training data to estimate the parameters necessary for classification.
| Property | Description |
|---|---|
| Output Mode | Select the mode in which you want to use the output of this algorithm. |
| Features | Select the input columns with which you want to perform the analysis. |
| Target Variable | Select the target column for which you want to perform the analysis. |
| Predicted Column Name | Enter a name for the newly created column that contains the predicted values. |
| Laplace Smoothing | Enter the smoothing constant for smoothing observations. Smoothing constant must be a double value greater than 0. Enter 0 to disable Laplace smoothing. |
| Missing Values | Select the method for handling missing values.
|
| Number of Threads | Enter the number of threads that the algorithm should use during execution. The default value is 1. |