Document custom ML models

src/docs/customization.md

@@ -134,3 +134,27 @@ Median absolute error      65.843924
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+### Using customized ML classifiers and regressors
+
+By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then
+selects the best one based on cross-validation performance. Alternatively, the user specify which model a component
+should use through the `classifier` or `regressor` contructor parameters. The provided classifiers and regressors must 
+follow the sklearn API. In particular, classifiers must provide the methods `fit`, `predict_proba` and `predict`,
+while regressors must provide the methods `fit` and `predict`
+
+!!! danger
+    MIPLearn must be able to generate a copy of any custom ML classifiers and regressors through 
+    the standard  `copy.deepcopy` method. This currently makes it incompatible with Keras and TensorFlow
+    predictors. This is a known limitation, which will be addressed in a future version.
+    
+The example below shows how to construct a `PrimalSolutionComponent` which internally uses
+sklearn's `KNeighborsClassifiers`. Any other sklearn classifier or pipeline can be used. 
+
+```python
+from miplearn import PrimalSolutionComponent
+from sklearn.neighbors import KNeighborsClassifier
+
+comp = PrimalSolutionComponent(classifier=KNeighborsClassifier(n_neighbors=5))
+comp.fit(train_instances)
+``` 
+