Dealing with Imbalanced Data in Machine Learning


As an ML engineer or data scientist, generally you inevitably end up in a state of affairs the place you’ve gotten a whole lot of information for one class label and hundreds of information for an additional class label.

Upon coaching your mannequin you acquire an accuracy above 90%. You then understand that the mannequin is predicting the whole lot as if it’s in the category with nearly all of information. Excellent examples of this are fraud detection issues and churn prediction issues, the place nearly all of the information are in the damaging class. What do you do in such a situation? That would be the focus of this submit.


Collect More Data

The most easy and apparent factor to do is to gather extra knowledge, particularly knowledge factors on the minority class. This will clearly enhance the efficiency of the mannequin. However, this isn’t at all times doable. Apart from the price one must incur, generally it is not possible to gather extra knowledge. For instance, in the case of churn prediction and fraud detection, you may’t simply look forward to extra incidences to happen so that you could accumulate extra knowledge.


Consider Metrics Other than Accuracy

Accuracy will not be a great way to measure the efficiency of a mannequin the place the category labels are imbalanced. In this case, it is prudent to contemplate different metrics akin to precision, recall, Area Under the Curve (AUC) — simply to say a couple of.

Precision measures the ratio of the true positives amongst all of the samples that had been predicted as true positives and false positives. For instance, out of the variety of folks our mannequin predicted would churn, what number of really churned?

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Recall measures the ratio of the true positives from the sum of the true positives and the false negatives. For instance, the share of people that churned that our mannequin predicted would churn.

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The AUC is obtained from the Receiver Operating Characteristics (ROC) curve. The curve is obtained by plotting the true constructive fee in opposition to the false constructive fee. The false constructive fee is obtained by dividing the false positives by the sum of the false positives and the true negatives.
AUC nearer to at least one is healthier, because it signifies that the mannequin is ready to discover the true positives.


Emphasize the Minority Class

Another strategy to deal with imbalanced knowledge is to have your mannequin give attention to the minority class. This could be accomplished by computing the category weights. The mannequin will give attention to the category with a better weight. Eventually, the mannequin will be capable to be taught equally from each lessons. The weights could be computed with the assistance of scikit-learn.

from sklearn.utils.class_weight import compute_class_weight
weights = compute_class_weight(‘balanced’, y.distinctive(), y)
array([ 0.51722354, 15.01501502])

You can then go these weights when coaching the mannequin. For instance, in the case of logistic regression:

class_weights = {
}lr = LogisticRegression(C=3.0, fit_intercept=True, warm_start = True, class_weight=class_weights)

Alternatively, you may go the category weights as balanced and the weights will likely be robotically adjusted.

lr = LogisticRegression(C=3.0, fit_intercept=True, warm_start = True, class_weight=’balanced’)

Here’s the ROC curve earlier than the weights are adjusted.

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And right here’s the ROC curve after the weights have been adjusted. Note the AUC moved from 0.69 to 0.87.

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Try Different Algorithms

As you give attention to the proper metrics for imbalanced knowledge, you can even check out totally different algorithms. Generally, tree-based algorithms carry out higher on imbalanced knowledge. Furthermore, some algorithms akin to LightGBM have hyperparameters that may be tuned to point that the information will not be balanced.


Generate Synthetic Data

You can even generate artificial knowledge to extend the variety of information in the minority class — often often known as oversampling. This is often accomplished on the coaching set after doing the prepare take a look at break up. In Python, this may be accomplished utilizing the Imblearn package deal. One of the methods that may be carried out from the package deal is called the Synthetic Minority Over-sampling Technique (SMOTE). The method is predicated on k-nearest neighbors.

When utilizing SMOTE:

  • The first parameter is a float that signifies the ratio of the variety of samples in the minority class to the variety of samples in the bulk class, as soon as resampling has been accomplished.
  • The variety of neighbors for use to generate the artificial samples could be specified by way of the k_neighbors parameter.
from imblearn.over_sampling import SMOTEsmote = SMOTE(0.8)X_resampled,y_resampled = smote.fit_resample(X.values,y.values)pd.Series(y_resampled).value_counts()0    9667
1    7733 
dtype: int64

You can then suit your resampled knowledge to your mannequin.

mannequin = LogisticRegression()mannequin.match(X_resampled,y_resampled)predictions = mannequin.predict(X_test)


Undersample the Majority Class

You can even experiment on decreasing the variety of samples in the bulk class. One such technique that may be carried out is the NearMiss methodology. You can even specify the ratio similar to in SMOTE, in addition to the variety of neighbors by way of n_neighbors.

from imblearn.under_sampling import NearMissunderSample = NearMiss(0.3,random_state=1545)pd.Series(y_resampled).value_counts()0  1110 1  333 dtype: int64


Final Thoughts

Other strategies that can be utilized embody utilizing constructing an ensemble of weak learners to create a powerful classifier. Metrics akin to precision-recall curve and space underneath curve (PR, AUC) are additionally price making an attempt when the constructive class is crucial.

As at all times, it’s best to experiment with totally different strategies and decide on those that provide the finest outcomes to your particular issues. Hopefully, this piece has given some insights on find out how to get began.

Code out there right here.

Bio: Derrick Mwiti is a data scientist who has an excellent ardour for sharing data. He is an avid contributor to the data science group by way of blogs akin to Heartbeat, Towards Data Science, Datacamp, Neptune AI, KDnuggets simply to say a couple of. His content material has been considered over one million occasions on the web. Derrick can be an creator and on-line teacher. He additionally trains and works with varied establishments to implement data science options in addition to to upskill their employees. Derrick’s studied Mathematics and Computer Science from the Multimedia University, he is also an alumnus of the Meltwater Entrepreneurial School of Technology. If the world of Data Science, Machine Learning, and Deep Learning curiosity you, you may need to verify his Complete Data Science & Machine Learning Bootcamp in Python course.

Original. Reposted with permission.



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