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7 Important model evaluation error metrics that everyone should know

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Predictive Modeling works on constructive feedback principle. You build a model. Get feedback from metrics, make improvements and continue until you achieve a desirable accuracy. Evaluation metrics explain the performance of a model. An important aspects of evaluation metrics is their capability to discriminate among model results.

Many ingenuous analyst, don’t even check model accuracy. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach.

Simply, building a predictive model is not your motive. But, creating and selecting a model which gives high accuracy on out of sample data. It is crucial to check accuracy of the model prior to computing predicted values.

In this industry, it´s consider different kinds of metrics to evaluate the models. The choice of metric completely depends on the type of model and the implementation plan of the model. After you are finished building your model, these 7 metrics will help you in evaluating your model accuracy.

1. Confusion Matrix is generally used only with class output models. Same holds for senstivity and specificity.
2. Gain and Lift Charts is mainly concerned to check the rank ordering of the probabilities.
3. Kolmogorov Smirnov Chart is a measure of the degree of separation between the positive and negative distributions.
4. AUC – ROC is again one of the popular metrics used in the industry.  The biggest advantage of using ROC curve is that it is independent of the change in proportion of responders.
5. Gini Coefficient is sometimes used in classification problems. Gini coefficient can be straigh away derived from the AUC ROC number.
6. Concordant – Discordant Ratio is again one of the most important metric for any classification predictions problem.
7. Root Mean Squared Error is the most popular evaluation metric used in regression problems. It follows an assumption that error are unbiased and follow a normal distribution

Cross Validation (Not a metric though!) is one of the most important concepts in any type of data modeling. It simply says, try to leave a sample on which you do not train the model and test the model on this sample before finalizing the model.