GBM - ICE
Last updated
Last updated
Gradient Boosting Machine is a machine learning algorithm that forms an ensemble of weakly predicted decision trees
It constructs a forward stage-wise additive model by implementing gradient descent in function space
Also known as MART (Multiple Additive Regression Trees) and GBRT (Gradient Boosted Regression Trees)
Dataset: Medical Cost Personal ; Target: charges
The data is trained by calling the GradientBoostingClassifier function from Scikit learn Library
Accuracy:
In this section we will interpret a GBM using ICE plots.
We use the Pycebox library and generate ICE plots for "Smoker" feature against out predicted output of "Charges"
ICE plot
In the above plot, we see multiple lines plotted. Each line corresponds to a row in our data. We can see that for some individuals BMI does not affect the charges. But for a few of them, a high BMI seems to increase the charges. Such interpretation can be very useful in showing people the repercussions of having a high BMI.
ICE plot with PDP line
The above plot shows the ICE plot along with the aggregation line shown in black. The aggregation line is the same as the PDP line. The PDP line shows that the overall effect of the age is not much, though the charges increase a small bit after a certain age.
Centered ICE plot
The Centered ICE plot is centering the curves at a certain point in the feature and displaying only the differences in prediction so that it is easy to interpret.
