I need to force my random forest model to learn one crucial relationship between features. How can I achieve that? Say, given 10 independent features as input to my RF model, when feature 1 and feature 3 are 100 (or less), my model output must be 5 despite the values of the other features. How can I teach that relationship to my RF model - so it will always obey that?   

I have tried adding repeated artificial data points to the training set where features 1 and 3 are less or equal to 100 and the output is always zero, but the RF model does not seem to understand/learn that relation just based on that. 

I also tried to play with (1) number of trees (2) number of nodes and (3) max nodes without any success.

I am using the mlr library in R to create my RF model. Thanks.
Machine learning algorithms are not to know, they predict. A tree-based ML algorithm will give you probabilities of being in each class, not whether it belongs to a particular class. By preparing your labels carefully; if your data has enough examples of which “feature 1 and 3 are less and equal to 100” and the result is 0, it is highly probable that your algorithm will predict its class with a strong accuracy. Obviously,  the strength of the result depends on what your ML task is, what is your data, how it is structured and what your hyperparameter settings are. I do not know if there is another way to bend the forest in such a manner. 

I know that is not the kind of an answer you expected, but I’ll be glad if I could help.