The shortcomings of the Random Forest algorithm are as follows:
1. Random Forests aren’t good at generalizing cases with completely new data.
For Example, If we know that the cost of one ice cream is $1, 2 ice-creams cost $2, and 3 ice-creams cost $3, then how much do 10 ice-creams cost? In such cases, Linear regression models can easily figure this out, while a Random Forest has no way of finding the answer.
2. Random forests are biased towards the categorical variable having multiple levels or categories. It is because the feature selection technique is based on the reduction in impurity and is biased towards preferring variables with more categories so the variable selection is not accurate for this type of data.