Upload
wade-ramirez
View
33
Download
5
Tags:
Embed Size (px)
DESCRIPTION
Final Project- Mining Mushroom World. Agenda. Motivation and Background Determine the Data Set (2) 10 DM Methodology steps (19) Conclusion. Motivation and Background. To distinguish between edible mushrooms and poisonous ones by how they look - PowerPoint PPT Presentation
Citation preview
Final Project-Mining Mushroom World
Agenda
• Motivation and Background
• Determine the Data Set (2)
• 10 DM Methodology steps (19)
• Conclusion
Motivation and Background
• To distinguish between edible mushrooms and poisonous ones by how they look
• To know whether we can eat the mushroom, to survive in the wild
• To survive outside the computer world
Determine the Data Set (1/2)
Source of data : UCI Machine Learning Repository
Mushrooms Database• From Audobon Society Field Guide • Documentation: complete, but missing statistical
information • Described in terms of physical characteristics • Classification: poisonous or edible • All attributes are nominal-valued
*Large database: 8124 instances (2480 missing values for attribute #12)
Determine the Data Set (2/2)
1. Past Usage• Schlimmer,J.S. (1987). Concept Acquisition Thro
ugh Representational Adjustment (Technical Report 87-19).
• Iba,W., Wogulis,J., & Langley,P. (1988). ICML, 73-79
2. No other mushrooms data
10 DM Methodology steps
Step 1. Translate the Business Problem
into a Data Mining Problema. Data Mining Goal: separate edible mushroom
s from poisonous ones
b. How will the Results be Used- increase the survival rate
c. How will the Results be Delivered- Decision Tree, Naïve Bayes, Ripper, NeuralNet
10 DM Methodology steps
Step 2. Select Appropriate Dataa. Data Source
– The Audubon Society Field guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf
– Jeff Schlimmer donated these data on April 27th, 1987
b. Volumes of Data- Total 8124 instances
- 4208(51.8%) edible; 3916(48.2%) poisonous
- 2480(30.5%) missing in attribute “stalk-root”
10 DM Methodology steps
Step 2. Select Appropriate Data
c. How Many Variables- 22 attributes- cap-shape, cap-color, odor, population, habitat and so
on……
d. How Much History is Required- no seasonality
*As long as we can eat them when we see them
10 DM Methodology stepsStep 3. Get to Know the Dataa. Examine Distributions: Use “Weka” to visuali
ze all the 22 attributes with histograms
b. Class: edible=e, poisonous=p
Step 3. Get to Know the Data
a. Examine Distributions: there are 2 types of historgrams
b. First- all kinds of values appear
c. (Attribute 21) population: abundant=a, clustered=c,
numerous=n, scattered=s, several=v, solitary=y
Step 3. Get to Know the Data
1. Examine Distributions: there are 2 types of historgrams– Second- only some kinds of value appear– (Attribute 7) gill-spacing: close=c, crowded=w, dist
ant=d
Step 3. Get to Know the Data
1. Examine Distributions: there are exceptions– Exception 1- missing values in the attribute– (Attribute 11) stalk-root: bulbous=b, club=c, cup=u,
equal=e, rhizomorphs=z, rooted=r, missing=?
2480 of this attribute have missing values (Total 8124)
Step 3. Get to Know the Data
1. Examine Distributions: there are exceptions– Exception 2- undistinguishable attribute
– (Attribute 16) veil-type: partial=p, universal=u
Step3. Get to Know the Data
2. Compare Values with Descriptions– no unexpected values except for missing values
10 DM Methodology steps
Step 4. Create a Model Set – Creating a Balanced Sample- 75%(6093) as
training data, 25%(2031) as test data– Rapid Miner’s “cross-validation” function: k-1 as
training, 1 as test
10 DM Methodology steps
Step 5. Fix Problems with the Data– Dealing with Missing Values- the attribute “stalk-
root” has 2480 missing values
– replace all missing values with the average of “stalk-root” value
– We replaced ‘?’ with the average value ‘b’
10 DM Methodology steps
Step 6. Transform Data to Bring Information
to the Surface
– all nominal attribute, no numerical analysis in this step
10 DM Methodology steps
Step 7. Build Model1. Decision Tree
Performance– Accuracy: 99.11%– Lift: 189.81%
True p True e Class precision
Pred. p 961 0 100%
Pred. e 18 1052 98.32%
Class recall 98.16% 100.00%True p True e Class precision
Pred. p 961 0 100%
Pred. e 18 1052 98.32%
Class recall 98.16% 100.00%
10 DM Methodology steps
Step 7. Build Model2. Naïve Bayes
Performance– Accuracy: 95.77%
– Lift: 179.79%
True p True e Class precision
Pred. p 902 9 99.01%
Pred. e 77 1043 93.12%
Class recall 92.13% 99.14%
True p True e Class precision
Pred. p 902 9 99.01%
Pred. e 77 1043 93.12%
Class recall 92.13% 99.14%
10 DM Methodology steps
Step 7. Build Model3. Ripper
Performance– Accuracy: 100%
– Lift: 193.06%
True p True e Class precision
Pred. p 979 0 100.00%
Pred. e 0 1052 100.00%
Class recall 100.00% 100.00%
True p True e Class precision
Pred. p 979 0 100.00%
Pred. e 0 1052 100.00%
Class recall 100.00% 100.00%
10 DM Methodology steps
Step 7. Build Model4. NeuralNet
Performance– Accuracy: 91.04%– Lift: 179.35%
True p True e Class precision
Pred. p 907 110 89.18%
Pred. e 72 942 92.90%
Class recall 92.65% 89.54%
True p True e Class precision
Pred. p 907 110 89.18%
Pred. e 72 942 92.90%
Class recall 92.65% 89.54%
10 DM Methodology steps
Step 8. Assess Models– Accuracy: Ripper and Decision Tree have b
etter performancesAccuracy
99.1195.77
100
91.04
85
90
95
100
105
DecisionTree
NaïveBayes
Ripper Neural Net
Accuracy
10 DM Methodology steps
Step 8. Assess Models– Lift (to compare the performances of different classific
ation models): Ripper and Decision Tree have higher lifts
Lift
189.81
179.79
193.06
179.35
170
175
180
185
190
195
1 2 3 4
Lift
10 DM Methodology steps
Step 9. Deploy Models– We haven’t go out and find real mushrooms
Step 10. Assess ResultsConclusion and questions– Maybe ripper and decision tree are better models
for nominal data
– How Rapid Miner separates training data from test data