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MeasureAvailable for
Confusion Matrix
  • Binary classification models
  • Multi-class classification models
  • Anomaly detection models
  • Deep learning models
Accuracy
  • Binary classification models
  • Multi-class classification models
  • Anomaly detection models
  • Deep learning models
ROC Curve
  • Binary classification models
AUC
  • Binary classification models
Feature Importance
  • Binary classification
  • Numerical prediction
Predicted vs Actual
  • Binary classification models
  • Multi-class classification models
  • Deep learning models
MSE
  • Numerical prediction
  • Recommendation models
Residual Plot
  • Numerical Prediction
Precision and RecallBinary classification Recall
  • Anomaly detection models
F1 Score
  • Binary classification Anomaly detection models

Confusion Matrix
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confusion matrix
confusion matrix

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If the above conditions are not satisfied, it is possible that there are some missing/hidden factors/predictor variables that have not been taken into account. Residual plot is available for numerical prediction models. You can select a dataset feature to be plotted with its residuals.

Precision and Recall
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Precision and Recall
Precision and Recall

Precision and Recall are performance measures used to evaluate search strategies. They are typically used in document retrieval scenarios.

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MeasureDefinitionFormula
PrecisionThe number of the records relevant to the search that are retrieved, as a percentage of the total number of records in the databaseselected items that are relevant. TP / (TP + FP)
Recall

The number of the records relevant to the search that are retrieved, as a percentage of the total number of records that are relevant to the searchitems that are selected.

Info

This is the same as the TPR.


TP / (TP + FN)

 

F1 Score
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F1 Score
F1 Score

The F1 Score gives the weighted average of Precision and Recall. It is expressed as a value between 0 and 1, where 0 indicates the worst performance and 1 indicates the best performance.

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