Soft stacking!

Soft stacking is a technique in machine learning that is used to combine predictions from several different models to improve overall accuracy and performance. In soft stacking, predictions from each model are used as input for another model called a "meta-model" or "stacking model."

The goals of soft stacking are to:

1. *Increase accuracy*: By combining predictions from several models, soft stacking can improve overall accuracy.

2. *Reduce overfitting*: Soft stacking can help reduce overfitting by combining predictions from different models.

3. *Increase robustness*: Soft stacking can make the model more robust to changes in data or different conditions.

Soft stacking is often used in machine learning competitions and has been proven effective in improving model performance. Would you like to know more about soft stacking or other machine learning techniques? #SoftStalking