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63dc46e Oct 1, 2015
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Comparing Model Evaluation Procedures

Training and testing on the same data

  • Goal is to estimate likely performance of a model on out-of-sample data
  • But, maximizing training performance rewards overly complex models that won't necessarily generalize
  • Unnecessarily complex models overfit the training data:
    • Will do well when tested using the in-sample data
    • May do poorly on out-of-sample data
    • Learns the "noise" in the data rather than the "signal"

Train/test split

  • Split the dataset into two pieces, so that the model can be trained and tested on different data
  • Testing performance is a better estimate of out-of-sample performance (compared to training performance)
  • But, it provides a high variance estimate since changing which observations happen to be in the testing set can significantly change testing performance
  • Allows you to easily inspect your testing results (via confusion matrix or ROC curve)

K-fold cross-validation

  • Systematically create "K" train/test splits and average the results together
  • Cross-validated performance is a more reliable estimate of out-of-sample performance (compared to testing performance)
  • Runs "K" times slower than train/test split

Comparing Evaluation Metrics for Classification Problems

Classification accuracy/error

  • Classification accuracy is the percentage of correct predictions (higher is better)
  • Classification error is the percentage of incorrect predictions (lower is better)
  • Easiest classification metric to understand

Confusion matrix

  • Confusion matrix gives you a better understanding of how your classifier is performing
  • Allows you to calculate sensitivity, specificity, and many other metrics that might match your business objective better than accuracy

ROC curves and Area Under the Curve (AUC)

  • Allows you to visualize the performance of your classifier across all possible classification thresholds, thus helping you to choose a threshold that appropriately balances sensitivity and specificity
  • Still useful when there is high class imbalance (unlike classification accuracy/error)
  • Harder to use when there are more than two response classes

Log loss

  • Most useful when well-calibrated predicted probabilities are important to your business objective

Comparing Evaluation Metrics for Regression Problems

Mean Absolute Error (MAE)

  • Mean of the absolute value of the errors
  • Easiest regression metric to understand

Mean Squared Error (MSE)

  • Mean of the squared errors
  • More popular than MAE, because MSE "punishes" larger errors, which tends to be useful in the real world

Root Mean Squared Error (RMSE)

  • Square root of the mean of the squared errors
  • Even more popular than MSE, because RMSE is interpretable in the "y" units