chore: import upstream snapshot with attribution

faq/choosing-technique.md

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