# Evaluation

UI Guide

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.

## Regression Models

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

*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 (R*^{2})- Measures how much better the model's predictions are than just predicting a single value for all examples.
- R
^{2}= 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 classification tasks, we provide some useful performance metrics and tools.

*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.

Updated about 2 months ago