Soft stacking!

Soft stacking is a technique in machine learning used to combine predictions from several different models to improve overall accuracy and performance. In soft stacking, the 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 enhance overall accuracy.

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

3. *Enhance robustness*: Soft stacking can make models more robust to changes in data or different conditions.

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