Dear Souvik Panda

Do you mean SMOTE for Imbalanced Classification?

@Shima Shafiee yes I mean SMOTE for imbalance classification. Can you please personally message me.I have shown your profile and found that your domain is matching with my work

Souvik Panda SMOTE (Synthetic Minority Oversampling Strategy) is an oversampling technique for improving machine learning model performance on unbalanced classification problems. Here are a few things you may attempt to improve SMOTE's performance on Google Colab:

1. Use a larger dataset: Using a larger dataset is one technique to boost SMOTE performance. This can assist SMOTE to create more varied synthetic samples, which can increase your model's generalization ability.

2. SMOTE's hyperparameters can be adjusted: SMOTE features numerous hyperparameters that can be adjusted to regulate the oversampling process. You can, for example, vary the percentage of synthetic samples created or the number of nearest neighbors utilized to generate the synthetic samples. You may experiment with various combinations of these hyperparameters to find which one provides the greatest performance.

3. Ensemble approaches, such as boosting and bagging, can increase SMOTE performance by training numerous models and aggregating their predictions. You may use ensemble approaches with SMOTE to determine whether they help your model perform better.

4. Use other oversampling techniques: You can use additional oversampling techniques in addition to or instead of SMOTE. Try the ADASYN (Adaptive Synthetic Sampling) or the Borderline SMOTE algorithms, for example. You may compare their performance to find which one works best for your dataset.

I hope these recommendations are useful! Please let me know if you have any more inquiries.