Hi,

This depends on your data, as there is no best and accurate machine learning algorithms that one can highlight blindly without experiments. Hence, you need to try many algorithms and select the best one.

HTH.

Dr. Samer Sarsam

Hi,

This depends on your data, as there is no best and accurate machine learning algorithms that one can highlight blindly without experiments. Hence, you need to try many algorithms and select the best one.

HTH.

Dr. Samer Sarsam

Dear Karim Hashim Kraidi Al-Saedi ,

As mentioned by Samer Sarsam , you need to test a set of classifiers and select the best one. This is because a classifier that achieves a good accuracy in a dataset can obtain a poor accuracy in other datasets. This problem is named "no free lunch theorem".

Thanks Samer and Artur

Based on the features in the dataset better to go with multi stage or hybrid algorithms which might give you higher accuracy

If someone answers you this question, he s doing it for points. The question you should ask is how to find an algorithm that suits your data best. You should provide some intel on your dataset, as the task of the classification, the dimensionality of it, the amount of data, the kind of data (discrete, images, etc..)

Perform algorithm spot check with your dataset.

Hello Karim,

I've read also about that, ending up with what Samer said.

However, in most of the read literatures, and after their evaluation of multiple machine learning classification algorithims, i found that Linear discriminant analysis (LDA), Naiive Bayes (NB) & Neural Network were the recurrent winners.

Regards,

Lubna

Dear Karim,

What do you mean by "prediction"? The forecasting problem? If so the output is usually continuous and the regression method instead of classification one is more suitable.

Regards,

We all tend to say, "it depends on the data", but actually it depends more on the nature of the problem domain. The data that's currently available to you may or may not characterize your domain problem adequately. In my experience, it's more effective to continually brainstorm and test new additional features and feature combinations than to jump from one ML algorithm to another, hoping for better auto-magic accuracy.

Thank you very much for All