In supervised machine learning, computers put data through a model to make predictions. Today, you'll see how decision trees can identify cancer cells.
Physicians diagnose cancer by analyzing suspect cells after a biopsy. Researchers at the University of Wisconson quantified biopsy images, so computers could too.
Under a microscope, cancer looks "primitive and aggressive," a chaotic agglomeration of cells with irregularly shaped, sized, and patterned nuclei.
Researchers quantified ten characteristics of cell nuclei in breast-cancer biopsy images.
For each biopsy, the researchers calculated every attribute's average, standard error, and highest values. So, the data has 30 features (a.k.a. predictors, variables).
90250
Benign
Computers prioritize features that contribute the most information to the model. Decision trees do this by analyzing the distribution of classes of observations.
While building a decision tree, computers divide the data points into homogenous groups.
The computer must find forks ("if-then" statements) that split the data into branches.
Adding additional forks can improve a tree's prediction accuracy. A tree with one fork is called a stump. One with many is called bushy tree.
Tree Depth | Total Error |
---|---|
1 | 7.7% |
Tree Depth | Total Error |
---|---|
2 | 5.2% |
Tree Depth | Total Error |
---|---|
3 | 2.4% |
Tree Depth | Total Error |
---|---|
4 | 1.1% |
Tree Depth | Total Error |
---|---|
5 | 0.6% |
Tree Depth | Total Error |
---|---|
6 | 0% |
A 0% error rate is indeed too good to be true. In our next installment, you'll learn about training & test errors, the trouble with trees, and great alternatives.