Prediction models predict future events or outcomes by analyzing different patterns in the past data. Refer the understand prediction model page to learn more.
To set up a prediction model, follow these four steps:
Step 1: Create a prediction model
Step 3: Verify the model summary, train and test model
Training data is the initial dataset that is used by the model to find patterns, make interpretations, and arrive at a prediction. You need to train the model so that it can perceive the input information correctly and make accurate decisions based on the information provided. This ensures that the model performs the way it's intended to. You can feed training data in two ways:
You can select the data stored in your application fields to be fed into the model as the training data.
If you do not have sufficient records in your application, but have your data stored in a file of CSV format, you can use this as training data for your prediction model.
After adding the training data, you can review the model details such as Model Name, Base Field/Column, and Dependent Fields/Columns. If you need to make any modifications, you can go back and do so. Otherwise, you can proceed to train the model.
Before you can actually use your prediction model in your application, you have to train it to predict favorable outcomes.
After the training is complete, the user can view the status of the model (trained, failed, and draft), the model type, the date it was created on and updated on, and other details as mentioned below.
Under this section, you can view the current version of your model, trained data count, base field, and dependent fields.
In this section, you can view the number of versions the model has, what version the model is currently running on, model creation date and its accuracy score. An accuracy score will appear for each trained version. You can retrain your model and use this accuracy score to quickly compare two versions of the same model.
In this section, you can view the App Name, Form Name, and the Field Names in which the model is deployed. You can also filter between different environments to check which environment a model is deployed in.
After training, you can test the model's reliability before deploying it in any of your applications. This is optional and ensures that the model analyzes the data patterns correctly and predicts the outcomes with high accuracy.
After testing your model, you'll get the predicted outcome along with an accuracy score. Evaluation of accuracy score
After you train your model, you need to publish it to make it available for deployment in your applications. Developers and users can now use your prediction model and start making predictions.
Retraining the model with the added records in your form periodically helps your model attain more precise results. Reworking on the model's efficiency allows the model to be tuned specifically to your business perspective.
After you train and test your model, you can publish it to make it available to your users and start making predictions.
You can now access your app in live and add values in the dependent fields. The prediction field will predict a value accordingly.
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