What are the effects of class weight in machine learning?
An example of an AI chatbot conversation about the impact of class weight in ML models.
What are the effects of class weight in machine learning?
Class weight helps to address class imbalance by assigning different weights to classes during training.
How does it improve model performance?
It allows the model to pay more attention to underrepresented classes, improving accuracy and reducing bias.
Can you give an example of when to use class weight?
Sure! In a dataset with 90% of one class and 10% of another, using class weight can help the model learn better from the minority class.
Are there any downsides to using class weight?
Yes, if not set properly, it can lead to overfitting on the minority class or underfitting on the majority class.
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