# Class Imbalance (CI)
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Class imbalance (CI) bias occurs when a facet value *d* has fewer training samples when compared with another facet *a* in the dataset. This is because models preferentially fit the larger facets at the expense of the smaller facets and so can result in a higher training error for facet *d*. Models are also at higher risk of overfitting the smaller data sets, which can cause a larger test error for facet *d*. Consider the example where a machine learning model is trained primarily on data from middle-aged individuals (facet a), it might be less accurate when making predictions involving younger and older people (facet d).

The formula for the (normalized) facet imbalance measure:

        CI = (na - nd)/(na \$1 nd)

Where na is the number of members of facet *a* and nd the number for facet *d*. Its values range over the interval [-1, 1]. 
+ Positive CI values indicate the facet *a* has more training samples in the dataset and a value of 1 indicates the data only contains members of the facet *a*.
+  Values of CI near zero indicate a more equal distribution of members between facets and a value of zero indicates a perfectly equal partition between facets and represents a balanced distribution of samples in the training data.
+ Negative CI values indicate the facet *d* has more training samples in the dataset and a value of -1 indicates the data only contains members of the facet *d*.
+ CI values near either of the extremes values of -1 or 1 are very imbalanced and are at a substantial risk of making biased predictions.

If a significant facet imbalance is found to exist among the facets, you might want to rebalance the sample before proceeding to train models on it.