If I get 95+ % accuracy in normal models, should I still consider Ensemble models? Why should I choose Ensemble models over normal models?

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    $\begingroup$ It pretty much depends on your data and your goal. $\endgroup$ – tagoma Dec 20 '17 at 9:12
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    $\begingroup$ If you take part in Kaggle or any other competition, then any tiny fraction of better score is useful. So, you can choose for competition very complex model with slightly better score over simple and fast model with almost the same score. For real life it's usually not the case. Competition winner solutions not often will be useful for real business cases. $\endgroup$ – CrazyElf Dec 20 '17 at 10:25

Firstly, welcome to the site!

When do we use Ensemble model?

when there are 2 models which perform moderately then we combine their results to get a model which performs better. In your scenario you already have a model which gives you good results what is the point of implementing Ensemble Models?

As @Tagoma said, it depends on your data and your goal. For example, you are trying to predict stock rates, every 0.01% matters. In such scenarios you need to use complex algorithms to maintain balance on that slim line i.e., not to over-fit, not to over-train and just predict.

Measures to check if your model is over-trained is by giving some random data and see how it performs, add some noise in the training data.

One more important thing to do is, to check for the Predictor Importance and see if there is any highly correlated feature with the target variable. For example, you are planning to predict age and if you have DOB as a feature in that yes of course you would predict with 99.99% accuracy but that is not what we use ML for.

If all of them are satisfied and achieving that accuracy then it means that your model's performance is good.

Finally, Implementation of Ensemble is dependent on your business problem and your understanding on business.

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  • $\begingroup$ Generally, the threshold which we keep is 90-95%, if we are crossing that threshold, it is most likely that the model is over-trained or data is over-fitting. Don't spread the misconception if you don't understand what you are saying!! $\endgroup$ – enterML Dec 21 '17 at 5:30
  • $\begingroup$ @Nain: can you please explain what you meant? do you mean that what I said is wrong? if so do explain it, will correct myself but that doesn't meant that I'm spreading something wrong! $\endgroup$ – Toros91 Dec 21 '17 at 5:38
  • $\begingroup$ There is no defined threshold. It's normal to achieve 98% accuracy on a task. It actually depends on the problem and the type of the data. 98% is no indication that the network is actually overfitting $\endgroup$ – enterML Dec 21 '17 at 9:27
  • $\begingroup$ In my case when I was working with a model and got 96% accuracy and when we re-checked it, it was because of over fitting. It is better if you check twice before deciding. So, based on my experience I was suggesting him to do so. But thank you will keep that in mind. I've removed that statement too $\endgroup$ – Toros91 Dec 21 '17 at 9:33
  • $\begingroup$ That's why we always keep validation and test sets to cross validate it but that doesn't mean it's bad $\endgroup$ – enterML Dec 21 '17 at 9:34

It is a problem specific and data specific problem. For one problem, 95% accuracy may be good, for some other it might not be very good and there might still be room for improvements. Likewise, it might happen that the problem is well defined, your architecture is good but due to a lot of noise in the data, you can't do better than 95%. In that case, we will say the results are good.

Ensembles: Ensembling is a general term for combining many classifiers by averaging or voting. It is a form of meta-learning in that it focuses on how to merge results of arbitrary underlying classifiers. Generally, ensembling improves the final results but again, not necessarily. If you are ensembling two highly correlated models, your ensembled model will give almost the same result as your stand-alone models.

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