[robotics-worldwide][news] How to choose the appropriate Machine Learning model ?
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One of the most common questions about Machine Learning right now is the choice of an ML model for a particular data set . How to choose ? Moreover which Hyper parameters will be best for my job ? I know the trendiest ML is Deep Learning, specially for Computer vision. But what about using a less computationally expensive model for an embedded system ?
I was working on a project to solve this problem. I have developed a Code which will show the best Machine Learning Classifiers from the six most famous Machine Learning Models along with the best hyper parameters for them .
I have tested the Code on Five famous data sets- Titanic, MNIST, CIFAR, Caltech and Machine Learning Donors Dataset from UCI repository. The First version is available here https://github.com/sezan92/Classifier . For MNIST, CIFAR and Caltech101 data sets I have used images instead of csv files to show the extraction of features , image preprocessing and labelling. Please use it and give me feedback .
If you have any question and/or feedback please contact via email email@example.com . Or The linkedin profile www.linkedin.com/in/sezan92