How would you handle an imbalanced dataset?

Answer 1

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Image classification is at the core of numerous AI applications, from facial recognition to autonomous driving. High-quality image classification datasets are essential for training machine learning models to accurately recognize and categorize objects across various environments. At GTS AI, we provide expertly curated datasets designed to enhance the accuracy and versatility of your AI models. Our datasets are meticulously labeled, covering diverse categories to suit different industry needs, whether for healthcare, retail, or smart city applications. Invest in reliable datasets with GTS AI and empower your AI systems to perform with precision and efficiency.

Answer 2

An imbalanced dataset is when you have, for example, a classification test and 90% of the data is in one class. That leads to problems: an accuracy of 90% can be skewed if you have no predictive power on the other category of data! Here are a few tactics to get over the hump:

1- Collect more data to even the imbalances in the dataset.

2- Resample the dataset to correct for imbalances.

3- Try a different algorithm altogether on your dataset.

What’s important here is that you have a keen sense for what damage an unbalanced dataset can cause, and how to balance that.