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mushroom

mushroom

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Author: Source: Unknown - Please cite: 1. Title: Mushroom Database 2. Sources: (a) Mushroom records drawn from The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) (c) Date: 27 April 1987 3. Past Usage: 1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational Adjustment (Technical Report 87-19). Doctoral disseration, Department of Information and Computer Science, University of California, Irvine. --- STAGGER: asymptoted to 95% classification accuracy after reviewing 1000 instances. 2. Iba,W., Wogulis,J., & Langley,P. (1988). Trading off Simplicity and Coverage in Incremental Concept Learning. In Proceedings of the 5th International Conference on Machine Learning, 73-79. Ann Arbor, Michigan: Morgan Kaufmann. -- approximately the same results with their HILLARY algorithm 3. In the following references a set of rules (given below) were learned for this data set which may serve as a point of comparison for other researchers. Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules from training data using backpropagation networks, in: Proc. of the The 1st Online Workshop on Soft Computing, 19-30.Aug.1996, pp. 25-30, available on-line at: http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/ Duch W, Adamczak R, Grabczewski K, Ishikawa M, Ueda H, Extraction of crisp logical rules using constrained backpropagation networks - comparison of two new approaches, in: Proc. of the European Symposium on Artificial Neural Networks (ESANN'97), Bruge, Belgium 16-18.4.1997, pp. xx-xx Wlodzislaw Duch, Department of Computer Methods, Nicholas Copernicus University, 87-100 Torun, Grudziadzka 5, Poland e-mail: duch@phys.uni.torun.pl WWW http://www.phys.uni.torun.pl/kmk/ Date: Mon, 17 Feb 1997 13:47:40 +0100 From: Wlodzislaw Duch Organization: Dept. of Computer Methods, UMK I have attached a file containing logical rules for mushrooms. It should be helpful for other people since only in the last year I have seen about 10 papers analyzing this dataset and obtaining quite complex rules. We will try to contribute other results later. With best regards, Wlodek Duch ________________________________________________________________ Logical rules for the mushroom data sets. Logical rules given below seem to be the simplest possible for the mushroom dataset and therefore should be treated as benchmark results. Disjunctive rules for poisonous mushrooms, from most general to most specific: P_1) odor=NOT(almond.OR.anise.OR.none) 120 poisonous cases missed, 98.52% accuracy P_2) spore-print-color=green 48 cases missed, 99.41% accuracy P_3) odor=none.AND.stalk-surface-below-ring=scaly.AND. (stalk-color-above-ring=NOT.brown) 8 cases missed, 99.90% accuracy P_4) habitat=leaves.AND.cap-color=white 100% accuracy Rule P_4) may also be P_4') population=clustered.AND.cap_color=white These rule involve 6 attributes (out of 22). Rules for edible mushrooms are obtained as negation of the rules given above, for example the rule: odor=(almond.OR.anise.OR.none).AND.spore-print-color=NOT.green gives 48 errors, or 99.41% accuracy on the whole dataset. Several slightly more complex variations on these rules exist, involving other attributes, such as gill_size, gill_spacing, stalk_surface_above_ring, but the rules given above are the simplest we have found. 4. Relevant Information: This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy. 5. Number of Instances: 8124 6. Number of Attributes: 22 (all nominally valued) 7. Attribute Information: (classes: edible=e, poisonous=p) 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. bruises?: bruises=t,no=f 5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s 6. gill-attachment: attached=a,descending=d,free=f,notched=n 7. gill-spacing: close=c,crowded=w,distant=d 8. gill-size: broad=b,narrow=n 9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y 10. stalk-shape: enlarging=e,tapering=t 11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=? 12. stalk-surface-above-ring: ibrous=f,scaly=y,silky=k,smooth=s 13. stalk-surface-below-ring: ibrous=f,scaly=y,silky=k,smooth=s 14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y 15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y 16. veil-type: partial=p,universal=u 17. veil-color: brown=n,orange=o,white=w,yellow=y 18. ring-number: none=n,one=o,two=t 19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z 20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y 21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y 22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d 8. Missing Attribute Values: 2480 of them (denoted by "?"), all for attribute #11. 9. Class Distribution: -- edible: 4208 (51.8%) -- poisonous: 3916 (48.2%) -- total: 8124 instances

23 features

class (target)nominal2 unique values
0 missing
stalk-surface-above-ringnominal4 unique values
0 missing
habitatnominal7 unique values
0 missing
populationnominal6 unique values
0 missing
spore-print-colornominal9 unique values
0 missing
ring-typenominal5 unique values
0 missing
ring-numbernominal3 unique values
0 missing
veil-colornominal4 unique values
0 missing
veil-typenominal1 unique values
0 missing
stalk-color-below-ringnominal9 unique values
0 missing
stalk-color-above-ringnominal9 unique values
0 missing
stalk-surface-below-ringnominal4 unique values
0 missing
cap-shapenominal6 unique values
0 missing
stalk-rootnominal4 unique values
2480 missing
stalk-shapenominal2 unique values
0 missing
gill-colornominal12 unique values
0 missing
gill-sizenominal2 unique values
0 missing
gill-spacingnominal2 unique values
0 missing
gill-attachmentnominal2 unique values
0 missing
odornominal9 unique values
0 missing
bruises%3Fnominal2 unique values
0 missing
cap-colornominal10 unique values
0 missing
cap-surfacenominal4 unique values
0 missing

107 properties

8124
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
2480
Number of missing values in the dataset.
2480
Number of instances with at least one value missing.
0
Number of numeric attributes.
23
Number of nominal attributes.
0.73
Average class difference between consecutive instances.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.01
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.01
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Entropy of the target attribute values.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.11
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
5.04
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
51.8
Percentage of instances belonging to the most frequent class.
4208
Number of instances belonging to the most frequent class.
3.03
Maximum entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0.91
Maximum mutual information between the nominal attributes and the target attribute.
12
The maximum number of distinct values among attributes of the nominal type.
Maximum skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
1.41
Average entropy of the attributes.
Mean kurtosis among attributes of the numeric type.
Mean of means among attributes of the numeric type.
0.2
Average mutual information between the nominal attributes and the target attribute.
6.11
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5.13
Average number of distinct values among the attributes of the nominal type.
Mean skewness among attributes of the numeric type.
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
48.2
Percentage of instances belonging to the least frequent class.
3916
Number of instances belonging to the least frequent class.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
5
Number of binary attributes.
21.74
Percentage of binary attributes.
30.53
Percentage of instances having missing values.
1.33
Percentage of missing values.
0
Percentage of numeric attributes.
100
Percentage of nominal attributes.
0.83
First quartile of entropy among attributes.
First quartile of kurtosis among attributes of the numeric type.
First quartile of means among attributes of the numeric type.
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
First quartile of skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
1.47
Second quartile (Median) of entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.17
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.05
Third quartile of entropy among attributes.
Third quartile of kurtosis among attributes of the numeric type.
Third quartile of means among attributes of the numeric type.
0.28
Third quartile of mutual information between the nominal attributes and the target attribute.
Third quartile of skewness among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
3.18
Standard deviation of the number of distinct values among attributes of the nominal type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

13 tasks

0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: stalk-surface-above-ring
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: veil-color
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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