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Distinguish Wild Mushrooms with Decision Tree. Shiqin Yan. Objective. Utilize the already existed database of the mushrooms to build a decision tree to assist the process of determine the whether the mushroom is poisonous . DataSet.

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objective
Objective
  • Utilize the already existed database of the mushrooms to build a decision tree to assist the process of determine the whether the mushroom is poisonous.
dataset
DataSet
  • Existing record drawn from the Audubon Society Field Guide to North American Mushrooms (1981) . G. H. Lincoff (Pres. ), NewYork: Alfred A. Knopf
  • Number of Instances: 8124 (classified as either edible or poisonous)
  • Number of Attributes: 22
  • Training: 5416, Tuning: 1354, Testing: 1354
  • Missing attribute values: 2480 (denoted by “?”), all for attribute 11
mushroom features
Mushroom Features
  • 1. cap-shape: bell=b, conical=c, convex=x, flat=f, knobbed=k, sunken = s
  • 2. cap-surface: fibrous=f, grooves=g, scaly=y, smooth=s
  • 3. cap-color: brown=n, buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y
  • 4. bruise?: bruises=t, no=f
  • 5. odor: almond=a, anise=l, creosote=c, fishy=y, foul=f
approach
Approach
  • Mutual information to determine the features used to split the tree.
  • Mutual information:
  • Y: label, X: feature
  • Choose feature X which maximizes I(Y;X)
most informative features extracted from decision tree
Most informative features extracted from decision tree:
  • odor
  • spore-print-color
  • habitat
  • population
prior research
Prior Research

by WlodzislawDuch, Department of Computer Methods, Nicholas Copernicus University

future
Future
  • Add cross-validation to improve the accuracy
  • Prune the tree to avoid over-fitting
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