Top machine learning companies introduce several techniques to improve the performance of a ML model. So far, we’ve gone over regularization, epochs and now stacking. Stacking is combining multiple machine learning models into one, which is then fine-tuned using a cross-validation process.
Stacking has many advantages, but mainly it focuses on combining the predictions of multiple models in order to produce more accurate predictions. This is extremely helpful when one or more of the specific models are not performing properly on their own. By combining the poorly performing models, you will receive better predictions compared to any of the individual models.
There are many techniques to stack machine learning models, each with its own set of pros and cons. The most common approach, and what we will focus here, is to stack multiple neural networks together. Combining each network that specializes in a different task will results in the desired outcome. One other approach is to use an algorithm that can automatically choose the best machine learning model for each new data set.
Stacking is an important process in machine learning and can be used to increase the performance of any poorly executing machine learning model. If you're having issues, and not getting the desired results out of your algorithm, you should consider using stacking in order to get the most out of your ML models.