 In this video, we're going to get into how we can plot a single tree. So similar to the classification video, we say model plotter dot plot model in the lab. And then we give it our model, our tree index. Here, I'll just plot the zero and our max depth. And again, I'll do three. And what you'll notice is that there's still a very similar process. So we still have our condition that's still getting split. But you'll notice there's none of those colored bars. And that's because this isn't classification, it's regression. So what this value is telling us is that this is the average value across every data set, every data point in the training set for this tree. So the average value was 329 cubic feet of natural gas. When we split based off of this total square footage value. So if your house is more than 2000 square feet, you end up over here. Suddenly this average is much higher. These are the larger houses takes more to heat. Whereas this value for smaller homes becomes smaller average. And what we can see is if we increase this depth to our full depth and see what the massive tree looks like and scroll to the end. And our final leaf nodes are are also averages so there are five values in this final leaf nodes. And on average, these 13 cubic feet of natural gas. If we go up to here, we've got one leaf node where we've got an average of 367 cubic feet, but another leaf node where they use none. So these this leaf note is a house that full of houses that have full electric heat. And so they don't. They don't have any natural gas usage. And so then you can go through and see, you know how you end up here so you've got square feet greater than this but you've got an oven you've got an older household. You've got fridges, you've got less and so forth. And so you can begin to see what these values look like. But to bring me down to three, it's a lot more manageable to see just this snapshot of what these important variables are within our model.