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Can you combine machine learning algorithms?
Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
How does machine learning use multiple algorithms?
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.
How do you combine different ML models?
Split the data into 2 fold. Do the mapping on the first fold for every algorithm/model. The other fold is used to train a last classifier that take a sample space of the length of the number of model and predict the label. This is the only method I know to combine different algorithm.
What does multiple model mean in machine learning?
Multiple-model machine learning refers to techniques that use multiple models in some way that closely resembles ensemble learning. Use of multiple models for multi-class classification and multi-output regression differ from ensembles in that no contributing member can solve the problem.
How is machine learning used in data science?
Data scientists commonly use machine learning algorithms, such as gradient boosting and decision forests, that automatically build lots of models for you. The individual models are then combined to form a potentially stronger solution. One of the most accurate machine learning classifiers is gradient boosting trees.
How is parallel training used in machine learning?
While the example will consist of training multiple machine learning models in parallel, I will provide a generic framework that can be used to launch arbitrary data tasks such as feature engineering and model metric computation. Some applications for multiple model parallel training are:
How is multi label classification used in machine learning?
Multi-label classification: Train multiple binary/multi-class classification models in parallel, where each model training task will use a different column as the label column, such that the resulting combination of models will effectively be a multi-label classifier;