Help Center/ ModelArts/ ModelArts User Guide (Standard)/ Using ExeML for Zero-Code AI Development/ Using ExeML for Predictive Analytics/ Training a Predictive Analysis Model
Updated on 2024-10-29 GMT+08:00
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Training a Predictive Analysis Model

After the ExeML task is created, a model is trained for predictive analytics. You can publish the model as a real-time inference service.

Procedure

  1. On the ExeML page of the new version, click the name of the target project to view the execution status of the current workflow.
  2. On the predictive analytics phase, wait until the phase status changes from Running to Executed.
  3. Click to view the training details, such as the label column, data type, accuracy, and evaluation result.

    The example is a discrete value of binary classification. For details about the evaluation result parameters, see Table 1.

    For details about the evaluation results generated for different data types of label columns, see Evaluation Results.

An ExeML project supports multiple rounds of training, and each round generates an AI application version. For example, the first training version is 0.0.1, and the next version is 0.0.2. The trained models can be managed by training version. After the trained model meets your requirements, deploy the model as a service.

Evaluation Results

The parameters in evaluation results vary depending on the training data type.

  • Discrete values

    The evaluation parameters include recall, precision, accuracy, and F1 score, which are described in the following table.

    Table 1 Parameters in discrete value evaluation results

    Parameter

    Description

    Recall

    Fraction of correctly predicted samples over all samples predicted as a class. It shows the ability of a model to distinguish positive samples.

    Precision

    Fraction of correctly predicted samples over all samples predicted as a class. It shows the ability of a model to distinguish negative samples.

    Accuracy

    Fraction of correctly predicted samples over all samples. It shows the general ability of a model to recognize samples.

    F1 Score

    Harmonic average of the precision and recall of a model. It is used to evaluate the quality of a model. A high F1 score indicates a good model.
  • Continuous values

    The evaluation parameters include Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The three error values represent a difference between a real value and a predicted value. During multiple rounds of modeling, a group of error values is generated for each round of modeling. Use these error values to determine the quality of a model. A smaller error value indicates a better model.