Predictor
Train the model to predict a column with a set of values.
Train the model to predict a column with a set of values.
The Predictor first determines if the problem looks like a classification or a regression. If it does, then this tool considers all classification modeling tools and regression modeling tools. Varicent AI uses the tool with the best score to make a prediction.
Note
The speed and quality slider is more of a spectrum. This setting determines the number of machine learning models considered. Models that do not support the training data are automatically excluded.
This tool adds two columns to your data: a prediction and a probability. It's the model's certainty about the prediction.
When you run the tool, the data is automatically split: 80% of the data is used for training. The remaining 20% is used for testing. Each model being considered is trained and evaluated to select the one with the best score. This is done 5 times to predict the test values (the 20% of your data). The final score is the average of all 5 scores.
Tip
You can configure this tool without using the configuration menu.
In the tab to auto-complete. Then start typing the name of the column you want to use and press tab to auto-complete.
menu, start typing the first few letters of the tool name and pressData profile chart visuals
From the Row viewer, you can access the Data profile link to open the column details and compare column visuals.
From the Row viewer, you can access the Data profile link to open the column details and compare column visuals. The results are available when you select one of the Classifier, Predictor or Regressor tools.
In your selected pipe, go to the Row viewer.
Click on the Data profile link in the row viewer.
The Data profile page opens with column details and compare column visuals.
Note
If you have an explainable tool upstream, you can still get an error message with one of the following issues:
The schema has changed in the export. For example, a missing column or an extra column is present.
There are multiple explainable tools in the pipe upstream.
The pipe changed, and the calculation is now invalid.
If there is no Data profile link, no explainable tool is selected in the upstream pipe.
When to use this tool
Use this when you want to make a prediction, but you're unfamiliar with classification and regression modelling tools. After you're comfortable with the difference between those tools, switch to using the Regressor or Classifier tool.

What is Smart exclude?
Following a successful build using the Predictor tool, Smart exclude identifies and automatically excludes columns that don’t help predict the target column. Smart exclude will only consider columns not already manually excluded. If you want to disable this setting to troubleshoot, test, or run a calculation that is taking too long, go to the Advanced settings under the Configure tab.
Configuration
Use the following configuration options to configure the Predictor tool.
Go to the Pipes module from the side navigation bar.
On the Pipes tab, find the pipe you want to work with. Click the pipe to open. For more information about pipes, see the Creating a pipe documentation.
In your pipe, add your data source.
Click
+ Tool.
In the Tools modal, search for Predictor. Click + Add Tool.
Tip
You can also find the Predictor tool in the Learn section.
Connect the tool to your dataset.
In the configuration pane, enter the following information:
Table 68. Predictor tool configurationField
Description
Target column
Select the column that you want to use the Predictor on.
Advanced section
Speed vs Quality slider
Use the slider to indicate if you want speed versus quality when the predictor is working.
Exclude columns
Select the column(s) that you want to exclude from the prediction.
Smart exclude
Select this option if you want to have Smart Exclude identify and automatically exclude columns that don’t help predict the target column after you build.
The Prediction and Probability columns appear with the predication and the probability of the prediction.
Also, check the Tool tab for more detail on the model.
Usage example
For example, you want to predict customer churn based on historical data. The dataset that you're working with includes various attributes about customers and whether they have already churned or not.
Using the dataset, the Churned
column is the one that we want to use to predict. We want the tool to analyze the data and model dependencies between the Age
, Tenure
, LastTransactionAmount
, and the other columns to predict the churn for a customer.

The original dataset is augmented with predicted Churned values and associated probabilities, helping the company forecast customer behaviour trends. This gives you the opportunity to take preemptive actions such as a targeted retention campaign for customers predicted to churn.