Data Mining - (Parameters | Model) (Accuracy | Precision | Fit | Performance) Metrics


Accuracy is a evaluation metrics on how a model perform.

Normal Accuracy metrics are not appropriate for evaluating methods for rare event detection

Problem type




How about the overall fit of the model, the accuracy of the model?

<math>R</math> is the correlation between predicted and observed scores whereas <math>R^2</math> is the percentage of variance in Y explained by the regression model.


List of several error calculations:

  • The squared error. The squared error is the sum of the squared difference between the actual value and the predicted value.

<MATH> \text{Squared error}= \sum_{i=1}^{n} \left (x^i - \sum_{j=0}^{k}{w_j}.{a_j^i} \right)^2 </MATH>

  • Mean Absolute Error:

<MATH> \text{Mean Absolute Error}= \frac{|p_1-a_1|+\dots+|p_n-a_n|}{n} </MATH>

  • Relative absolute error:

<MATH> \text{Relative absolute error}= \frac{|p_1-a_1|+\dots+|p_n-a_n|}{|a_1-\bar{a}|+\dots+|a_n-\bar{a}|} </MATH>

  • Root relative squared error:

<MATH> \text{Root relative squared error}= \sqrt{\frac{(p_1-a_1)^2+\dots+(p_n-a_n)^2}{(a_1-\bar{a})^2+\dots+(a_n-\bar{a})^2}} </MATH>


The accuracy metrics are calculated with the help of a Machine Learning - Confusion Matrix


<MATH> \begin{array}{rrc} Accuracy & = & \frac{\text{Number of correct predictions}}{\text{Total of all cases to be predicted}} \\ & = & \frac{a + d}{a + b + c + d} \end{array} </MATH>

Accuracy is not really a reliable metric for the real performance of a classifier when the number of samples in different classes vary greatly (unbalanced target) because it will yield misleading results.

For example, if there were 95 cats and only 5 dogs in the data set, the classifier could easily be biased into classifying all the samples as cats. The overall accuracy would be 95%, but in practice the classifier would have a 100% recognition rate for the cat class but a 0% recognition rate for the dog class

The (error|misclassification) rates are good complementary metrics to overcome this problem.

Null Rate

The accuracy of the baseline classifier.

The baseline accuracy must be always checked before choosing a sophisticated classifier. (Simplicity first)

Accuracy isn’t enough. 90% accuracy need to be interpreted against a baseline accuracy.

A baseline accuracy is the accuracy of a simple classifier.

If the baseline accuracy is better than all algorithms accuracy, the attributes are not really informative.

Error rate



The true accuracy is the accuracy calculated on the entire data set (no data set split)

Documentation / Reference

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