Decision Trees are strong tools we use in supervised learning. They have some great strengths, like:
Easy to Understand: Decision Trees show a clear flow of choices, which helps us see how decisions are made. For example, it’s simple to look at how customers are grouped based on their habits.
Versatile: They can work well with both numbers and categories of information.
But, Decision Trees also have some downsides:
Overfitting: Sometimes, Decision Trees can get too complicated, especially when they go really deep. This can mean they only fit the data we have and don’t do well with new data.
Unstable: Even little changes in the data can create very different trees.
It's important to find the right balance between these strengths and weaknesses to use Decision Trees effectively.
Decision Trees are strong tools we use in supervised learning. They have some great strengths, like:
Easy to Understand: Decision Trees show a clear flow of choices, which helps us see how decisions are made. For example, it’s simple to look at how customers are grouped based on their habits.
Versatile: They can work well with both numbers and categories of information.
But, Decision Trees also have some downsides:
Overfitting: Sometimes, Decision Trees can get too complicated, especially when they go really deep. This can mean they only fit the data we have and don’t do well with new data.
Unstable: Even little changes in the data can create very different trees.
It's important to find the right balance between these strengths and weaknesses to use Decision Trees effectively.