Why Imbalanced data matters in Machine Learning

Biased Model: Machine learning models tend to be biased towards the majority class. In your case, a model might become very good at predicting "Rejected" but perform poorly on "Accepted" instances.

Poor Generalization: The model might not learn the characteristics of the minority class ("Accepted") well enough to generalize to unseen data.

Misleading Accuracy: A high overall accuracy can be misleading. For example, a model that always predicts "Rejected" would achieve 66% accuracy on your data, but it would be useless for your actual goal of predicting both "Accepted" and "Rejected" status.

Is Your Imbalance "Highly" Imbalanced?

While a 2:1 ratio is a moderate imbalance, it can still impact model performance. Whether it's "highly" imbalanced depends on the specific problem and the complexity of the data.  A 2:1 ratio might be manageable with the right techniques, but it's definitely something you need to address.

What Can You Do?

Here are several strategies to handle imbalanced data:

Resampling Techniques:

Oversampling the Minority Class: Increase the number of "Accepted" instances.

Random Oversampling: Duplicate existing "Accepted" samples.  Simple but can lead to overfitting.

SMOTE (Synthetic Minority Over-sampling Technique): Create new synthetic "Accepted" samples by interpolating between existing ones.  More sophisticated and generally preferred.

ADASYN (Adaptive Synthetic Sampling Approach): Similar to SMOTE, but generates more synthetic samples for "Accepted" instances that are harder to learn.

Undersampling the Majority Class: Decrease the number of "Rejected" instances.

Random Undersampling: Randomly remove "Rejected" samples.  Can lead to information loss.

Cluster Centroids: Replace clusters of "Rejected" samples with the cluster centroids.

Tomek Links: Remove pairs of very close instances that have opposite classes.

Cost-Sensitive Learning:

Assign different weights to the classes during model training.  Give higher weight to the minority class ("Accepted") to penalize misclassifications more heavily.  Most machine learning libraries (like scikit-learn) have built-in support for class weights.

Ensemble Methods:

Balanced Bagging/Random Forest: Create balanced subsets of the data through bootstrapping.

EasyEnsemble/BalanceCascade: Train several models on balanced subsets and combine their predictions.

Change the Evaluation Metric:

Don't rely on accuracy.  Use metrics that are more robust to class imbalance:

Precision and Recall: Focus on the performance for each class separately.

F1 Score: Harmonic mean of precision and recall, balances both.

AUC-ROC: Area Under the Receiver Operating Characteristic curve.

AUPRC: Area Under the Precision-Recall Curve.  Often preferred for highly imbalanced data.

Data Augmentation:

If your data involves text, you can use techniques like synonym replacement, back-translation, or random insertion/deletion to create more "Accepted" samples.

Recommendation for Your Case

Given your 2:1 ratio, I'd recommend starting with these approaches:

Resampling: Try SMOTE or ADASYN to oversample "Accepted," or try Tomek links for under sampling "Rejected".

Cost-Sensitive Learning: Use class weights in your chosen model (e.g., class_weight='balanced' in scikit-learn).

Evaluation Metrics: Use F1 score, AUC-ROC, and AUPRC to evaluate your model, not just accuracy.

By addressing the class imbalance, you'll create a model that is more accurate and reliable in predicting both "Accepted" and "Rejected" statuses.