wehub-resource-sync
50 lines
1.9 KiB
Markdown
50 lines
1.9 KiB
Markdown
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# What factors should I consider when choosing a predictive model technique?
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This is a very broad question, and the answer would basically fill an entire book. In a nutshell, I would come up with the
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### 1. How does your target variable look like?
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- continuous target variable? -> regression
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- categorical (nominal) target variable? -> classification
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- ordinal target variable? -> ranked classification
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- no target variable and want to find structure in data? -> cluster analysis, projection
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### 2. Is computational performance an issue?
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- use "cheaper" models/algorithms
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- dimensionality reduction
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- feature selection
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- lazy learner (e.g,. k-nearest neighbors)
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### 3. Does my dataset fit into memory? If no:
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- out of core learning
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- distributed systems
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### 4. Is my data linearly separable?
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- hard to know the answer upfront
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- always a good idea to compare different models
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### 5. Finding a good bias variance threshold. Does my model overfit?
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- increase regularization strength if supported by the model
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- dimensionality reduction or feature selection otherwise
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- collect more training data if possible (check via learning curves first)
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### 6. Are you planning to update your model with new data on the fly?
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- one option are lazy learners (e.g., K-nearest neighbors); needs to keep training data around; no learning necessary but more expensive predictions
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- it's generally relatively cheap to update generative models
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- another option is stochastic gradient descent for online learning
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...
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The list goes on and on :). I think Andreas Mueller's scikit-learn algorithm "cheat-sheet" is an excellent resource. (Click on the image to view the original, interactive version on scikit-learn)
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[](http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html)
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[Source: http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html]
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