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I'm kinda new to machine learning and wanted to know if we could use multiple machine learning algorithms, for example, SVM and backpropagation together to solve a particular problem.

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closed as too broad by Stephen Rauch, Sean Owen Nov 14 '18 at 6:23

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ yes u can..ex: .i have done random forest for regression problem and again i applied linear regression for the output...the same way based on u r problem u can do $\endgroup$ – sai saran Nov 14 '18 at 14:48
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You can train multiple machine learning models with same data and based on accuracy and confusion_matrix response you can decide which one to use.

In theory, you don't need to combine/merge two different ML Models (you can enhance your pre-processing) but if you still want to use different models there are two approaches:

  1. Ensemble (available with three categories - a) Bagging b) Boosting c) Stacking
  2. Hybrid (This approach allow users to create own models (or use existing) and combine them for better prediction)

Note: You need to be careful with individual algorithm response before combining them together :)

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In a classification/regression task you can use back propagation and SVM:

  • Backpropagation: use a neural network as feature extractor
  • SVM: use it to perform classification/regression with the features extracted with the neural network

In deed, in neural networks back-propagation and other well-known machine learning techniques are used together. For example, when a sigmoid layer is used as the classification layer for a binary classification neural net, a logistic regression is performed and optimized through back-propagation

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