Working with Predictive Models
The Models List
The Analytics Builder provides a Models list page where all of the existing models for every available dataset are listed. The list includes the following information:
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Column Name
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Description
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Model Name
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The name of a specific model. The name must be unique.
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Dataset Name
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The name of the dataset the model is based on.
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Goal
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List of the variables in a dataset that can serve as goals, (also known as dependent variables).
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Filter
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A filter that was applied to the dataset when the model was created. All models must have at least the all_data filter applied.
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State
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The current status of the model. When you first create the model, the state is "Queued" and then "Running." When the new model is created and ready for use, the state changes to "Completed." If the job fails, the state will show "Failed."
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Confidence Level %
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A percentage used to calculate confidence intervals during the training process. When scoring a predictive model that includes confidence intervals, the confidence level indicates the likelihood that the actual score or prediction falls within a specified range. The default value is 80%. Multiple confidence models can be generated by including multiple Confidence Level values separated by commas.
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ROC
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Represents the area under the ROC (Receiver Operating Characteristics) curve. This statistic indicates how well the model separates positives and negatives. It’s a measurement of the model's ability to correctly classify predictions as true or false across various discrimination thresholds. Displays when either a ROC curve or a confusion matrix graph is shown.
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RMSE
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Root Mean Square Error is a measurement of the difference between values predicted by the model and the values actually observed. A low RMSE value is better than a high value. For CONTINUOUS and ORDINAL goals.
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RMSE Normalized
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The normalization is done by dividing the RMSE value by the difference between the MAX and MIN values of the goal variable if such values are provided in the dataset metadata. If the MAX and MIN values are not set in the dataset metadata, the normalization is achieved by dividing the RMSE value by the standard deviation of the goal field over the training set.
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Pearson Correlation
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A measure of the linear correlation (or dependence) between the predicted and actual results. Values can range between -1 (total negative correlation) and +1 (total positive correlation). For CONTINUOUS and ORDINAL goals.
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MCC
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Matthews Correlation Coefficient is a measurement of the quality of binary classifications. It takes into account the distribution of true and false positives in comparison with the distribution of true and false negatives. For BOOLEAN or CATEGORICAL goals.
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Accuracy
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A statistical measure, expressed in a percentage, that indicates how well the model predicted both successful and failed outcomes. For BOOLEAN and CATEGORICAL goals.
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Number of Records in Validation Set
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The number of records used to create the validation set. This value is based on the Validation Holdout % selected when the model was created.
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Created Date
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The date the model was created.
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Model List Options
To create a new model, click New and follow the steps in Creating New Predictive Models.
To work with an existing model, select it in the models list and use one of the following options:
• Delete – Removes the selected model.
• View – Opens the Model Results page with a detailed view of the model statistics.
• Job Details – Opens the Model Job Details page and displays run time information for the model training and validation jobs.
• Retrain – Generates a new model using the same configuration as the selected model. A prompt will request a new model name. The old model is retained along with any associated scoring jobs.
• Publish – Publishes the completed model to Analytics Manager and opens it there as an analysis model
• Export – Downloads the completed model as a PMML file. Only one model can be downloaded at a time.
• Copy Job ID – Automatically copies the Model Job ID to the system clipboard.
• Prev/Next – Pages back or forward through the list of models when the list is too long to display all at once.
Filtering the Model List
To filter the list of models displayed in the chart, click Add Filter and select from the list of columns (example: "Goals"). Then identify the conditions for filtering the column (example: Contains "Pump"). Click Save (only datasets with "Pump" included in the Goals column will be displayed).