Hello!
In this video, we'll be covering the advantages and disadvantages of Decision Trees
Let's begin by examining the advantages and
disadvantages of using a decision tree?
The first main advantageof using a decision
tree is that it's easy to understand and,
as such, requires little data preparation.
A decision tree also runs in logarithmic time.
Another advantage is that we can also check
the accountability of the model using statistical
tests.
In fact, it's possible to calculate entropy
and information-gain using formulas.
The first disadvantage to consider is that,
with newer users, it's easy to create a tree
that over-fits the data
We also see that small variations in data
may result in completely different trees,
since the kind of splits that are made can
change this includes changes to the classification
of data as well.
Please note, however, that this can be fixed
with random forests.
Finally, decision trees use a greedy algorithm.
This results in a faster runtime, but may
not produce an optimal tree.
Once again, this can also be fixed with random
forests.
Thanks for watching!
