Predict a label from numeric values (train model)
Create a model that can learn how to accurately predict a label from a set of numeric values (train model).
Example: Consider a table with columns longitude, latitude, and a city name. Create a model that can predict a city name (or the closest city) from the given longitude and latitude.
29°45′46", 95°22′59″ => Houston, TX, USA
Operator Usage in Easy Mode
- Click + on the parent node.
- Enter Create Model from Numeric Values operator in the search field and select the operator from the Results to open the operator form.
- In the Table drop-down, enter or select the table to create a model.
- In the Model Name field, enter the name of the model to store it in a particular location.
- In the Label Column Name drop-down, select the column name that contains the labels. The drop-down list displays the data based on the selected table name.
- Optional. Click Add More to add a list of column names used to create the model.
- Click Run to view the result.
- Click Save to add the operator to the playbook.
- Click Cancel to discard the operator form.
createModelFromNumericValues(table, outputModelName, label, listOfColumns)
table: Input table to train a model.
outputModelName: Model name to store trained model. Trained models are not displayed in the UI, so remember the name of the model that was created. If a model with the same name exists, this operation overwrites it without notification.
label: Label column name.
listOfColumns: List of columns to train a model to learn label, such as "col1", "col2", "col3".
A message saying that the model (name) was successfully created.
table = github_logs
createModelFromNumericValues(table,"genderFromHeightWeight", "gender", "height_in", "weight_lb")
If the operation is successful, a message that the model was successfully created. To use in prediction, see the predictLabelFromNumericValues operator.
Updated about 2 years ago