mushroom edibility prediction with a vector support classifier
Suppose we'd like to make a vector support classifier to help us decide whether a mushroom might be edible. Disclaimer: I personally don't eat mushrooms that I find in the forest and, if I did, I definitely wouldn't use a machine learning algorithm alone to decide which ones to eat!! Now that's out of the way, let's see how well we can do.
The "class" field represents "poisonous" with a "p" and "edible" with "e"; the other fields encode other mushroom properties similarly. We'll need to convert these letters into numerical data; for that we'll use the python command "ord":
We are only able to achieve about 63% accuracy this way, with 730 poisonous mushrooms classified as edible. We can do better by including more mushroom attributes, for example cap-color and gill-color:
We've gone up to 86% accuracy and now misclassify 400 poisonous mushrooms as edible. If we keep adding attributes then the accuracy increases, but of course that also means we have more work to do in describing the mushroom we've found.