What are the best techniques for handling imbalanced datasets in Python?

1 Sampling methods

One of the most common ways to deal with imbalanced datasets is to use sampling methods, which either reduce the number of samples from the majority class (undersampling) or increase the number of samples from the minority class (oversampling). Undersampling can help remove noise and outliers from the majority class, but it can also discard useful information and reduce the diversity of the data. Oversampling can help create more balanced data by replicating or generating new samples from the minority class, but it can also introduce overfitting and duplication issues. Some popular sampling methods in Python are RandomUnderSampler, RandomOverSampler, TomekLinks, and ClusterCentroids from imbalanced-learn, and SMOTE, ADASYN, and BorderlineSMOTE from SMOTE.

2 Cost-sensitive learning

Another way to deal with imbalanced datasets is to use cost-sensitive learning, which assigns different weights or costs to different classes, making the model more sensitive to the minority class and less sensitive to the majority class. This can help improve the performance of the model on the imbalanced data without changing the data distribution. Cost-sensitive learning can be implemented in Python by using the class_weight parameter in some scikit-learn classifiers, such as LogisticRegression, RandomForestClassifier, and SVC. Alternatively, you can use cost-sensitive classifiers from imbalanced-learn, such as CostSensitiveLogisticRegression, CostSensitiveRandomForestClassifier, and CostSensitiveSVC.

3 Evaluation metrics

A third way to deal with imbalanced datasets is to use appropriate evaluation metrics, which can measure the performance of the model on the imbalanced data more accurately and fairly. The usual metrics, such as accuracy, precision, and recall, can be misleading and biased when applied to imbalanced datasets, as they can favor the majority class and ignore the minority class. Therefore, it is better to use metrics that take into account the balance of the classes, such as F1-score, Cohen's kappa, Matthews correlation coefficient, and area under the ROC curve. These metrics can be calculated in Python by using the metrics module from scikit-learn, such as f1_score, cohen_kappa_score, matthews_corrcoef, and roc_auc_score.

4 Ensemble methods

A fourth way to deal with imbalanced datasets is to use ensemble methods, which combine multiple models to achieve better results than a single model. Ensemble methods can help reduce the variance and bias of the models, as well as increase the diversity and robustness of the predictions. Some examples of ensemble methods are bagging, boosting, and stacking, which can be implemented in Python by using the ensemble module from scikit-learn, such as BaggingClassifier, AdaBoostClassifier, and StackingClassifier. Additionally, you can use ensemble methods that are specifically designed for imbalanced datasets, such as BalancedBaggingClassifier, BalancedRandomForestClassifier, and RUSBoostClassifier from imbalanced-learn, and SMOTEBoost, SMOTEBagging, and SMOTEENN from SMOTE.

5 Data augmentation

A fifth way to deal with imbalanced datasets is to use data augmentation, which is the process of creating new data from existing data by applying various transformations, such as rotation, scaling, cropping, flipping, and noise addition. Data augmentation can help increase the size and diversity of the data, as well as reduce overfitting and improve generalization. Data augmentation is commonly used in computer vision and natural language processing, where there are many possible ways to modify the data without changing its label. Some examples of data augmentation libraries in Python are torchvision, imgaug, and nlpaug, which provide various tools and methods for augmenting images and text.

6 Feature engineering

A sixth way to deal with imbalanced datasets is to use feature engineering, which is the process of creating new features or modifying existing features to improve the quality and relevance of the data. Feature engineering can help extract more information and patterns from the data, as well as reduce the dimensionality and complexity of the data. Some examples of feature engineering techniques are feature selection, feature extraction, feature scaling, feature encoding, and feature interaction. Feature engineering can be performed in Python by using various libraries and methods, such as SelectKBest, PCA, StandardScaler, OneHotEncoder, and PolynomialFeatures from scikit-learn, and Featuretools, which is a library for automated feature engineering.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?