Stacking Different Machine Learning Models to Improve the Performance

Stacking different learning models have been given a better output in the terms of the accuracy and the performance. There are majorly 3 types of machine learning algorithm they are supervised learning, unsupervised learning and Reinforcement learning. But the third type is not much considered the only first two are used for the classification and processing of the data. Just defining in one line I will say supervised learning is where the data is already known and the processing is done on a whole dataset. In unsupervised learning model is the training of the Ai algorithm on the unlabelled and non-classified data. These learning models and model stacking are now a days used for classification and also for image and voice recognition. Stacking provides us with high accuracy output but it is restricted if the data is not diverse or the models generate similar output. This paper presents the method to tackle this problem that is by stacking the to machine learning models that is stacking unsupervised learning with supervised learning. By these method the accuracy, recall and precision increases which is useful when working on a huge data set or the real time data set.