Class Imbalance: a classification headache

Class Imbalance: a classification headacheGonza Ferreiro VolpiBlockedUnblockFollowFollowingJun 25What is class imbalance?If you have had the chance of working around classification problems, then is probable you have faced a problem of imbalanced classes.

As in ‘Where’s Wally’ (or ‘Waldo’ depending on where are you from) we used to struggle to find our red and white friend, a class imbalance usually makes it harder for us to identify (and hence classify) a minority class.

This occurs in datasets with a disproportionate ratio of observations.

In other words, in a binary classification problem, you’d have a lot of elements of a class and very few from another.

But this could also happen in a multi-classification problem when the vast majority of the observations are clustered in one category or we have one category that’s highly under-represented in comparison with the rest.

The imbalance problem is not defined formally, so there’s no ‘official threshold to say we’re in effect dealing with class imbalance, but a ratio of 1 to 10 is usually imbalanced enough to benefit from using balancing techniques.

Why is this a problem?Most machine learning algorithms assume data equally distributed.

So when we have a class imbalance, the machine learning classifier tends to be more biased towards the majority class, causing bad classification of the minority class.

The Accuracy ParadoxThe Accuracy Paradox refers to the utility of using the Accuracy out of our Confusion Matrix as a metric for predictive modelling when classifying imbalanced classes.

Let’s take the following example to illustrate this better: suppose you’re working on a binary classification problem and you model scores 90% accuracy.

That sounds great, but when you dive a little deeper you discover that 90% of the data belongs to one class.

This means you’re predicting well only the majority class.

For this reason, Precision and Recall are better measured in cases like this.

Let’s remember a little about all these metrics and where they come from:Google ImagesAnother interesting metric to use in cases like this is the ROC curve:Baptiste Rocca, Handling imbalanced datasets in machine learning (2019), Towards Data ScienceThis metric shows the relationship between the Recall and the True Negative Rate or Specificity (True Negatives + False Negatives / True condition Positive).

The ideal point here would be when we have a Recall of 1 and a Specificity of also 1, because in that case, we would be predicting all the Positive values as Positive, and all the Negative values as Negative, but in practice that’s usually hard and we’ll try to play with the threshold of our classification algorithm to maximize the area under the ROC.

Also in a multi-classification problem, we’ll have as many curves as classes we may have.

About the separabilityLet’s remember first that while in Linear Regression we’re trying to fit a line using least squares, in a classification algorithm such a Logistic Regression, we don’t have the same concept of a ‘residual’, so it can’t use least squares and it can’t calculate R2.

Instead, Logistic Regression uses something called ‘maximum likelihood’:Google ImagesIn the previous image, we can notice that the predictor variable X has some overlap between y=0 and y=1.

This is a simple example of two classes that are slightly separable.

We’ll have cases where classes will overlap more and others where instead it will not overlap at all:Baptiste Rocca, Handling imbalanced datasets in machine learning (2019), Towards Data ScienceLinking this with the Theoretical Minimal Error Probability, the best possible classifier will choose for each point X the most likely of the two classes and for a given point X, the best theoretical error probability is given by the less likely of these two classes.

As in the example above, for a classifier with one feature and two classes, the theoretical minimal error probability is given by the area under the minimum of the two curves.

In cases where our predictor variable is well separable and we don’t have any overlap in between classes, the two classes are separated enough to compensate the imbalance.

The following image shows the effect of a good or bad separability over the ROC curve:Google ImagesBut what can we do when we don’t have a good separability in between classes and we do have class imbalance?Techniques to fight imbalanced dataIf we cannot collect more data or our classes are naturally imbalanced, here are a few techniques we can use to improve our classification performance.


Up-sample minority classUp-sampling is a simple process about randomly duplicating observations from a minority class.

You can import the resample module from sklearn.

utils (there’s also available the RandomUnderSampler module from imblearn.

under_sampling), separate observations from the minority class into a new DataFrame and run the following code:Elite Data Science, How to Handle Imbalanced Classes in Machine Learning (2017)2.

Down-sample majority classSimilar to the previous technique, but in this case removing random observations.

Elite Data Science, How to Handle Imbalanced Classes in Machine Learning (2017)The negative side of this is, as in the example, is that if we have few observations we would be reducing our dataset and probably affecting our predicting power.


Generate Synthetic SamplesSynthetic samples are artificially generated from the original data sample.

The most commonly used algorithms for generating synthetic data are SMOTE and ADASYN.

The first one creates new samples based on the distances between the point and its nearest neighbours.

SMOTE calculates the distances for the minority samples near the decision boundary and generates the new samples.

Let’s look at an example of how SMOTE works:Source: Google ImagesThe key difference between ADASYN and SMOTE is that the former uses a density distribution, as a criterion to automatically decide the number of synthetic samples that must be generated for each minority sample by adaptively changing the weights of the different minority samples to compensate for the skewed distributions.

The latter generates the same number of synthetic samples for each original minority sample.


Change the performance metricAs we talk earlier, Accuracy is not the right metric to use when we’re working with imbalanced data.

Instead, we could use for example Recall, Precision or ROC curves.


Try different algorithmsSome algorithms as Support Vector Machines and Tree-Based algorithms are better to work with imbalanced classes.

The former allows us to use the argument class_weight=’balanced’ to penalize mistakes on the minority class by an amount proportional to how under-represented it is.

Meanwhile, Decision Trees often perform well on imbalanced datasets because their hierarchical structure allows them to learn signals from both classes.

Thanks for reading!Special thanks to the following sources of inspiration:https://en.









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