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Model Evaluation

Model performance evaluation is one of the key tasks in the ML model lifecycle. A model's performance indicates how successful the model is at making useful predictions on data.

Once your trained model is loaded into Fiddler, click on Evaluate to see its performance.

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Regression Models

To measure model performance for regression tasks, we provide some useful performance metrics and tools.

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  • Root Mean Square Error (RMSE)
    • Measures the variation between the predicted and the actual value.
    • RMSE = SQRT[Sum of all observation (predicted value - actual value)^2/number of observations]
  • Mean Absolute Error (MAE)
    • Measures the average magnitude of the error in a set of predictions, without considering their direction.
    • MAE = Sum of all observation[Abs(predicted value - actual value)]/number of observations
  • Coefficient of Determination (R2)
    • Measures how much better the model's predictions are than just predicting a single value for all examples.
    • R2 = variance explained by the model / total variance
  • Prediction Scatterplot
    • Plots the predicted values against the actual values. The more closely the plot hugs the y=x line, the better the fit of the model.
  • Error Distribution
    • A histogram showing the distribution of errors (differences between model predictions and actuals). The closer to 0 the errors are, the better the fit of the model.

Classification Models

To measure model performance for regression tasks, we provide some useful performance metrics and tools.

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  • Precision
    • Measures the proportion of positive predictions which were correctly classified.
  • Recall
    • Measures the proportion of positive examples which were correctly classified.
  • Accuracy
    • Measures the proportion of all examples which were correctly classified.
  • F1-Score
    • Measures the harmonic mean of precision and recall.
  • AUC
    • Measures the area under the Receiver Operating Characteristic (ROC) curve.
  • Log Loss
    • Measures the performance of a classification model where the prediction input is a probability value between 0 and 1. The goal of the ML model is to minimize this value.
  • Confusion Matrix
    • A table that shows how many predicted and actual values exist for different classes. Also referred as an error matrix.
  • Receiver Operating Characteristic (ROC) Curve
    • A graph showing the performance of a classification model at different classification thresholds. Plots the true positive rate (TPR), also known as recall, against the false positive rate (FPR).
  • Precision-Recall Curve
    • A graph that plots the precision against the recall for different classification thresholds.
  • Calibration Plot
    • A graph that tell us how well the model is calibrated. The plot is obtained by dividing the predictions into 10 quantile buckets (0-10th percentile, 10-20th percentile, etc.). The average predicted probability is plotted against the true observed probability for that set of points.
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