Mentioning the problem domain, dataset type and size will help to answer this question.
Additional to Ferdib Al Islam it might be impossible to give an definitive yes/no answer to such questions. Article Classifier Technology and the Illusion of Progress
A general statement about the superiority of one algorithm over the others cannot be made. If possible, a large number of different algorithms should be evaluated with identical training and testing data. Please also keep in mind that the final prediction accuracy is extremely dependent on hyperparameter tuning, which should be performed for XGBoost, Random Forest as well as Decision Trees.
That's an interesting question and I would follow the argumentation of Marko Kureljusic - to evaluate which AI algorithm is the most appropriate one, you need to know your problem. There is not one single AI algorithm outperforming all others in every possible task.
In most cases the type of dataset you have determines which algorithm is better. Preferably, use a number of algorithms on your data and select the best. No single algorithm can be generalized as the best, as there are some factors that determine its performance.
It is based on the dataset and you can apply algorithms like Decision tree, Random Forest, and XGBoost. you can identify the in which algorithm's significant results with comparison reports. Finally, you can recommend which algorithm is the best result.
As others have already pointed our, there isn't a definitive answer to your question, as it depends on your data, though I would suggest to use either Random Forest or XGBoost. XGBoost is getting more and more popular because it tends to perform at least as good as RF, but you will have to spend more time on tuning the many hyperparameters. On the other hand, RF is easier to use and works fairly well out of the box.
Try both and compare the results. If both give you a similar performance, I would stick with Random Forest since it is easier to interpret and is already well established in the academic literature.
Further details about your study is required. Generally, I do prefer random forest, but comparing the results of both approaches is an excellent approach
DEAR Ashifur Rahman
Your question is thought-provoking and has no definitive answer
It is based on the dataset and how to train your model. In general, in my opinion, is RF because of some reasons such as:
For most reasonable cases, xgboost will be significantly slower than a properly parallelized random forest. If you're new to machine learning, I would suggest understanding the basics of decision trees before you try to start understanding boosting or bagging
The biggest advantage of Random forest is that it relies on collecting various decision trees to arrive at any solution. This is an ensemble algorithm that considers the results of more than one algorithm of the same or different kinds of classification.
Xgboost does capture non linear relationships. It has performed well on many tabular datasets with a fair amount of data. It produces smaller trees compared to RandomForest and can fit data better than a single on boosted tree.
GOOD LUCK
There is no specific answer to your question. Best option is to do a literature review on the ML techniques (supervised, unsupervised, semi-supervised etc).
I found this review paper really great to understand.
Article Random forest in remote sensing: A review of applications an...
- Badges
- Science topic
- Similar topics
- Engineering
- Materials Engineering
- Powders