Suppose you have a classification problem, now what if I implement and train all classification models like logistic regression, KKN, naive Bayes, decision tree or random forest on the training data set (we can change our data set to big or small according to the model). And now what I do is keep these classifiers for various model as the node for the neural network and update their weights on the premise of who gives the correct answer. Now I am just a noob when it comes to machine learning, but can someone please settle my curiosity.
Why can't we just use machine learning to select which model to use for a given model?
machine learning;neural network
You can. This is called an ensemble model. For example, a linear regression between the solutions of different predictive models is a way to take a weighted average of different models. Normally, the winners of all of the machine learning contests use ensemble models since they eek out a bit more accuracy. However, they are a lot more costly to train.