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| Measure | Available for |
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| Confusion Matrix |
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| Accuracy |
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| ROC Curve |
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| AUC |
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| Feature Importance |
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| Predicted vs Actual |
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| MSE |
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| Residual Plot |
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Confusion Matrix
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This method is available for binary classification and multi class classification models. |
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The confusion matrix is a table layout that visualises the performance of a classification model by displaying the actual and predicted points in the relevant grid.The The confusion matrix for a binary classification is as follows:
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Accuracy
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The accuracy of a model can be calculated using the following formula.
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You can find this metric for classification models in the model summary as shown in the above image.
ROC
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Curve
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This illustrates the performance of binary classifier model by showing the TPR (True Positive Rate> against the SPC (False Positive Rate) for different threshold values. A completely accurate model would pass through the 0, 1 coordinate (i.e. TPR of 1 and SPC of 0) in the upper left corner of the plot. However, this is not achievable in practical scenarios. Therefore, when comparing models, the model with the ROC curve closest to the 0, 1 coordinate can be considered the best performing model in terms of accuracy. The best threshold for this model is the threshold associated with the point that is closest to the 0,1 coordinate on the ROC curve. You can find ROC curve for a particular binary classification model under the model summary in WSO2 ML UI.
AUC
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AUC (Area Under the Curve) is another metric for accuracy of a binary classification model that is associated with the ROC curve. A model with greater accuracy should have a value closer to 1 for AUC (area of the ROC curve). Therefore, when comparing the accuracy of multiple models using the AUC, the one with the highest AUC can be considered the best performing model.
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Feature Importance
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This chart visualizes the importance (weight) of each feature in the model according to its significance in creating the final model. In regression models (numerical predictions), each of these weights represents the amount by which the response variable would be changed, when the respective predictor variable is increased by one unit. By looking at this chart you can make decisions for feature selection. This chart type is available for both binary classification and numerical prediction models.
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This chart plots the data points according to the correctness of the classification. You can select two dataset features to be visualized and the plot will display data distribution with the classification accuracy (correct/incorrect) for each point.
MSE
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MSE (Mean Squared Error) is the average of the squared errors of the prediction. An error is the difference between the actual value and the predicted value. Therefore, a better performing model should have a comparatively lower MSE. This metric is widely used to evaluate the accuracy of numerical prediction models. You can find this metric for numerical prediction models in the model summary as shown in the above image.
Residual Plot
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Residual plot shows the residuals on the y-axis and a predictor variable (feature) on the x-axis. A residual is defined as the difference between the observed (actual) value and the predicted value of the response variable. A model can be considered accurate then the residual points are:
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