Improving Machine learning model for trading and trend prediction

Question

I am working on making predictions and decisions based on stocks and crypto data.

First I implemented a decision tree model and I had Model Accuracy: 0.5. After that I did some research and found out that decision tree is not enough and I tried to improve it with random forest and adaboosting.

After that I noticed that I have 3 above mentioned algorithms with the same training and test data, and I get three different results.

Now the question is if it is possible to make the three algorithms work together by combining them in some way and benefit from the previous result?

Answer 1

You can combine classifiers, yes. This is considered an ensemble. It's a bit weird to make an ensemble from a decision tree and a random forest, though. A random forest is an ensemble of decision trees. That's why it's called a forest.