In machine learning, handling imbalanced datasets can be a challenge. This is especially true for classification tasks when the number of instances within different classes are significantly unbalanced. This can lead to models that are biased and perform poorly for minority classes. Several techniques can be used to handle imbalanced data effectively. In this answer, I will describe various methods to reduce the impact of unbalanced data on machine-learning models. Data Science Course in Pune
- Dataset Resampling:
- Subsampling : The goal of undersampling is to reduce the number instances in the class that has the largest majority to achieve an even distribution. It is possible to remove instances randomly from the majority class. However, this may lead to the loss of important information. Undersampling techniques such as Tomek Links or Edited Nearest Neighbors can be used intelligently to select instances that should be removed while maintaining the decision boundaries of classes.
- Oversampling : Oversampling is the process of increasing the number instances in the minorities class to balance a dataset. Overfitting can be achieved by duplicating instances. Synthetic Minority over-sampling Technique, a more advanced approach, generates synthetic samples using features from existing instances. This creates a diverse and representative dataset.
- Hybrid Methods: A hybrid method combines both undersampling (undersampling) and oversampling (oversampling) techniques to produce a balanced distribution. Examples of hybrid methods include SMOTE with Tomek Links (SMOTE – Tomek) and SMOTE with ENN links (SMOTE – ENN). These methods are designed to improve classification by simultaneously reducing and increasing the majority class.
- Algorithmic Techniques:
- Cost Sensitive Learning: Several machine learning algorithms let you assign different costs for misclassification to different classes. You can direct the model towards a more accurate prediction by increasing the costs associated with misclassifying instances in the minority class. Data Science Classes in Pune
- Ensemble Methods : Ensemble techniques like bagging and boosting are effective for handling unbalanced data. These methods improve performance and generalization by combining multiple classifiers. Boosting algorithms such as Adaptive Boosting or Gradient Boosting machine (GBM) assign higher weights for misclassified samples, thereby giving greater importance to minor class samples.
- Threshold adjustment: When dealing with unbalanced datasets, it can be beneficial to adjust the classification threshold. You can improve recall by moving the threshold in favor of the minorities. This may result in a reduction of precision. Therefore, the trade-off should be carefully considered between precision and recall.
- Data Augmentation :
- Data augmentation involves creating new instances of data by applying different transformations. It is especially useful for computer vision tasks where images can easily be rotated, cropped, zoomed or flipped to create new samples. The addition of minorities can improve the model’s performance and increase its representation.
- Algorithm selection:
- Some algorithms are better suited to imbalanced datasets. Support vector machines (SVMs) with classweights, random forest, and XGBoost, for example, have demonstrated good performance when dealing with imbalanced data. In addition, neural networks that use techniques such as focal loss or weighting can handle data imbalances effectively. Data Science Training in Pune
- Evaluation Metrics
- When dealing with unbalanced datasets, accuracy is not a reliable measure because it may be misleading due the class distribution. It is better to use evaluation metrics which focus on the minorities, such as precision or recall, F1 scores, or the area under the receiver-operating characteristic curve. These metrics allow a comprehensive evaluation of model performance when dealing with imbalanced datasets.
