You can build an AI model to make predictions on your connected data by clicking on “Add Step,” and then “Predict.”
Then, you’ll need to select the column to predict.
For example, in the Churn Prediction Demo, that column is simply called “Churn.” This is an example of classification, as the model will predict that a customer will either churn or not.
The “Training Mode” section gives you several options for model training:
Fastest (10 seconds)
High quality (1 minute)
Highest quality (5 minutes)
The “highest quality” selection means more training time, but it won’t always lead to higher accuracy, due to a phenomenon known as “overfitting,” which is when a machine learning model only learns the training data well but does not generalize to new data. Feel free to try out various training modes, as you won’t be charged at all for training time.
In any case, upon clicking “Predict,” a model and model report will be generated, as seen below.
To build a forecasting model, the steps are exactly the same, in that you connect an input dataset, and select a column to predict.
However, the resulting model and model report will be different. In a forecasting model, the “prediction quality” will be shown as a value that predictions are “usually within,” as well as an RMSE score. Further, the “sample predictions” will be quantitative values, instead of classes like “Yes” or “No.”