When using a decision tree model on a given training dataset the accuracy keeps improving with more and more splits. You can easily overfit the data and doesn't know when you have crossed the line unless you are using cross validation (on training data set).
The advantage of a simple decision tree is model is easy to interpret, you know what variable and what value of that variable is used to split the data and predict outcome.
A random forest is like a black box and works as mentioned in above answer. It's a forest you can build and control. You can specify the number of trees you want in your forest(n_estimators) and also you can specify max num of features to be used in each tree. But you cannot control the randomness, you cannot control which feature is part of which tree in the forest, you cannot control which data point is part of which tree. Accuracy keeps increasing as you increase the number of trees, but becomes constant at certain point. Unlike decision tree, it won't create highly biased model and reduces the variance.
When to use to decision tree:
- When you want your model to be simple and explainable
- When you want non parametric model
- When you don't want to worry about feature selection or regularization or
worry about multi-collinearity.
- You can overfit the tree and build a model if you are sure of
validation or test data set is going to be subset of training data
set or almost overlapping instead of unexpected.
When to use random forest :
- When you don't bother much about interpreting the model but want
- Random forest will reduce variance part of error
rather than bias part, so on a given training data set decision tree
may be more accurate than a random forest. But on an unexpected
validation data set, Random forest always wins in terms