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Model Evaluation

Model performance evaluation is one of the key tasks in the data science process. It indicates how successful the trained model is at scoring a dataset.

Once your trained model is loaded into Fiddler, you should be able to click on Evaluation to see its performance.


Regression Models

To measure model performance for Regression tasks, we have a few tools like

  • Coefficient of determination called R2
    • Measures how well the actual outcomes are replicated by the model
    • R2 = Variance explained by the model / Total Variance
  • 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
  • Root mean square error (RMSE)
    • Shows the variation between the predicted and the actual value.
    • RMSE = SQRT[Sum of all observation (predicted value - actual value)^2/number of observations]


Classification Models

To measure model performance for Classification tasks, we use the following tools:

  • 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 tells us how many actual values and predicted values exist for different classes. Also referred as 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).
  • 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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