How can you deal with imbalanced datasets when using machine learning?

Handling imbalanced datasets in machine learning can be challenging. Data that is imbalanced can have a negative impact on machine learning models. Data Science Training in Pune

1. Dataset Resampling:
   • Subsampling: To achieve an equal distribution, it is possible to randomly remove instances from the majority class. This can lead to the loss important information. Instead, undersampling techniques like Tomek Links and Edited Nearest neighbors are used to intelligently select instances to be removed.
   • Oversampling: This is the process by which you increase the number of instances in a minorities class.
   • A Hybrid Method combines undersampling and oversampling techniques to create a balanced distribution. Examples include SMOTE using Tomek Links and SMOTE using ENN Links. This method is designed to improve classification, by simultaneously reducing the majority class and increasing it.
2. Algorithmic Techniques:
   • Data Science Classes in Pune: You can assign different costs to misclassifications by using machine learning algorithms. This will help you direct the model toward a more accurate forecast.  Data Science Classes in Pune
   • Ensemble methods : These methods combine multiple classifiers to improve performance and generalization. Boosting algorithm such as Adaptive Boosting (ABM) or Gradient Boosting Machine (GBM), assigns higher weights for misclassified sample, thereby giving more importance to minor classes samples.
   • Threshold Adjustment: If you have unbalanced data, you may want to adjust the threshold. However, this can reduce precision. The trade-off between precision and recall should be carefully considered.
3. Data Augmentation :
   • Data Augmentation is the process of creating new data instances by applying different transformations. This technique is particularly useful for computer vision tasks, where images can be easily rotated, cropped or zoomed to create new samples. Adding minorities improves a model's performance, and increases its representation.
4. Algorithm selection:
   • Data Science Training in Pune - Some algorithms work better with imbalanced datasets. Random forest, SVMs with classweights and XGBoost are examples.  Data Sciences Course in Pune
5. Evaluation Metrics
   • Accuracy is not a reliable metric when dealing with unbalanced data because the class distribution can be misleading. Instead, it is best to use metrics that focus on minorities such as precision, recall, F1 or the area beneath the receiver-operating characteristics curve. This allows a comprehensive evaluation model performance in the face of imbalanced datasets.