fix link to remove_terms function

docs/interpret/hyperparameters.md

@@ -33,7 +33,7 @@ ideal: As many as possible within interpretability limits.
 
 hyperparameters: [0.0, 0.9, 0.95, 0.99, 100, 250, 1000]
 
-guidance: Generally, this parameter should be chosen based on interpretability considerations as having too many interactions makes the model less interpretable. A reasonable stragegy is to initially include more interactions than desired, then drop the less important interactions in post processing after fitting. See the :meth:`interpret.glassbox.ExplainableBoostingClassifier.remove_terms` function. In terms of model performance, introducing more interactions tends to improve model accuracy. Values between 0 and LESS than 1 are interpreted as percentages of the number of features. For example, a dataset with 100 features and an interactions value of 0.7 will automatically detect and use 70 interactions. Values of 1 or higher indicate the exact number of interactions to be detected, so for example 1 would create 1 interaction term, and 50 would create 50.
+guidance: Generally, this parameter should be chosen based on interpretability considerations as having too many interactions makes the model less interpretable. A reasonable stragegy is to initially include more interactions than desired, then drop the less important interactions in post processing after fitting. See the [remove_terms](./python/api/ExplainableBoostingClassifier.ipynb#interpret.glassbox.ExplainableBoostingClassifier.remove_terms) function. In terms of model performance, introducing more interactions tends to improve model accuracy. Values between 0 and LESS than 1 are interpreted as percentages of the number of features. For example, a dataset with 100 features and an interactions value of 0.7 will automatically detect and use 70 interactions. Values of 1 or higher indicate the exact number of interactions to be detected, so for example 1 would create 1 interaction term, and 50 would create 50.
 
 ## inner_bags
 default: 0