How to Handle Imbalanced Datasets in Predictive Modeling for Accurate Results?

Question

How to Handle Imbalanced Datasets in Predictive Modeling for Accurate Results?

Tanishq Sakhare 0

I am working on a predictive modeling project where the dataset is highly imbalanced (e.g., 90% of data belongs to one class, while the remaining 10% belongs to another). This imbalance is causing poor model performance, as the model tends to favor the majority class.

What are the best strategies to handle imbalanced datasets?

Should I use techniques like SMOTE, weighted loss functions, or ensemble methods?

Also, how do I evaluate model performance in such cases—should I focus more on precision-recall instead of accuracy?

Looking for recommendations and practical implementations to improve model performance.

Any guidance would be greatly appreciated! 🚀

VSawhney 715 Reputation points Microsoft External Staff Moderator

2025-04-03T16:18:34.2166667+00:00
Hello Tanishq Sakhare,

In order to handle imbalance datasets, you may use few techniques:

Oversampling: Duplicate existing samples in the minority class to increase representation. This approach is simple but can lead to overfitting, as the model may memorize repeated samples instead of generalizing.

SMOTE (Synthetic Minority Oversampling Technique): Generates synthetic data points for the minority class by interpolating between existing samples. SMOTE is more sophisticated than basic oversampling as it reduces overfitting risk.

With imbalanced datasets, accuracy is often misleading because it reflects majority class performance. Instead, you may use these metrics:

Confusion Matrix: Analyze false positives and false negatives, which are crucial in imbalance scenarios.

Precision-Recall Curve & AUC: Focuses on how well the model identifies minority class cases.

F1-Score: A balance of precision and recall, suitable when both metrics are equally important.

You can directly use Azure AutoML services, as it automatically detects and mitigates class imbalances using class weighting or sampling techniques.

You may refer the below links for more clarity on usage of Azure AutoML services:
Link 1: What is automated machine learning (AutoML)?
Link 2: Identify models with imbalanced data

Hope this helps. Please let us know if you need any further assistance.

Thank you!
VSawhney 715 Reputation points Microsoft External Staff Moderator

2025-04-04T08:50:46.71+00:00

Hello Tanishq Sakhare,

I hope you went through the suggestion provided and solved your issue. If you any further query, please feel free to reach us.

Thank you!
VSawhney 715 Reputation points Microsoft External Staff Moderator

2025-04-07T06:30:15.9933333+00:00

Hello Tanishq Sakhare,

Following up to check if you went through the suggestion provided and it solved your issue. If you any further query, please feel free to reach us.

Thank you!

1 answer

Your answer

VSawhney 715 Reputation points Microsoft External Staff Moderator

2025-04-03T16:18:34.2166667+00:00

Hello Tanishq Sakhare,

In order to handle imbalance datasets, you may use few techniques:

Oversampling: Duplicate existing samples in the minority class to increase representation. This approach is simple but can lead to overfitting, as the model may memorize repeated samples instead of generalizing.

SMOTE (Synthetic Minority Oversampling Technique): Generates synthetic data points for the minority class by interpolating between existing samples. SMOTE is more sophisticated than basic oversampling as it reduces overfitting risk.

With imbalanced datasets, accuracy is often misleading because it reflects majority class performance. Instead, you may use these metrics:

Confusion Matrix: Analyze false positives and false negatives, which are crucial in imbalance scenarios.

Precision-Recall Curve & AUC: Focuses on how well the model identifies minority class cases.

F1-Score: A balance of precision and recall, suitable when both metrics are equally important.

You can directly use Azure AutoML services, as it automatically detects and mitigates class imbalances using class weighting or sampling techniques.

You may refer the below links for more clarity on usage of Azure AutoML services:
Link 1: What is automated machine learning (AutoML)?
Link 2: Identify models with imbalanced data

Hope this helps. Please let us know if you need any further assistance.

Thank you!
VSawhney 715 Reputation points Microsoft External Staff Moderator

2025-04-04T08:50:46.71+00:00

Hello Tanishq Sakhare,

I hope you went through the suggestion provided and solved your issue. If you any further query, please feel free to reach us.

Thank you!
VSawhney 715 Reputation points Microsoft External Staff Moderator

2025-04-07T06:30:15.9933333+00:00

Hello Tanishq Sakhare,

Following up to check if you went through the suggestion provided and it solved your issue. If you any further query, please feel free to reach us.

Thank you!

Answer 1

Hello Tanishq Sakhare,

You're absolutely right, imbalanced datasets can severely impact model performance by biasing predictions toward the majority class. Here are proven strategies and best practices to improve results, especially in environments like Azure Machine Learning:

1. Data-Level Solutions - These techniques balance the dataset before training:

a. SMOTE (Synthetic Minority Over-sampling Technique)

SMOTE generates synthetic samples of the minority class to balance the dataset. It works well for structured data. You can implement it using imblearn in your training script or via a custom step in Azure ML pipelines.

b. Random Oversampling / Undersampling

Oversampling: Replicate minority class samples.

Undersampling: Remove samples from the majority class. It may cause loss of valuable information.

2. Algorithm-Level Solutions

a. Class Weighting / Cost-sensitive Learning

Most models (e.g., LogisticRegression, RandomForest, XGBoost) allow setting class_weight='balanced' or providing custom class weights.

b. Weighted Loss Functions (for Neural Networks)

In frameworks like TensorFlow/Keras or PyTorch, you can assign higher loss penalties to the minority class.

3. Model-Level Approaches

a. Ensemble Methods

Try models like Balanced Random Forest, EasyEnsemble, XGBoost with scale_pos_weight parameter. These handle imbalance better through internal reweighting or boosting.

4. Evaluation Metrics (Beyond Accuracy)

Accuracy is misleading in imbalanced datasets. Instead, focus on:

Precision & Recall

F1-Score (harmonic mean of precision and recall)

AUC-ROC or AUC-PR (Precision-Recall curve)

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How to Handle Imbalanced Datasets in Predictive Modeling for Accurate Results?

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