From the course: Supervised Learning Essential Training

Unlock the full course today

Join today to access over 22,600 courses taught by industry experts or purchase this course individually.

Analyzing decision tree performance

Analyzing decision tree performance - Python Tutorial

From the course: Supervised Learning Essential Training

Start my 1-month free trial

Analyzing decision tree performance

“

- [Instructor] Now that we've come up with predictions, let's have a look at our tree. This is our root node of our tree because it provided us with the largest split between our samples. You can see within that node, we started with 7,500 examples, and we're able to split one group in about 5,600 examples and another into 1800. If the light wasn't over 369, then all of those samples are evaluated by their CO2 level. If we keep following down this path, we check to see if their CO2 is at a certain point. And if it's not, we check to see if the humidity is at a certain point. This then splits again down between temperature. And if we scroll all the way over here between CO2 again. All of these decisions finally lead us to leaf nodes at the very bottom. What we can see is that the Gini index is zero, which is what we want. Meaning all of the samples in each of the groups are homogenous. We can also see how many…

Contents