machine learning in practice lecture 3 carolyn penstein rosé language technologies institute/...
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Machine Learning in PracticeLecture 3
Carolyn Penstein Rosé
Language Technologies Institute/ Human-Computer Interaction
Institute
Plan for Today Announcements
Assignment 2Quiz 1
Weka helpful hints Topic of the day: Input and Output More on cross-validation ARFF format
Weka Helpful Hints
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Weka Helpful Hint: Documentation!!
Click on Morebutton!
Output Predictions Option
Output Predictions Option
Important note: Because of the way Weka randomizes the data forcross-validation, the only circumstance under which you can matchthe instance numbers to positions in your data is if you have separate train and test sets so the order will be preserved!
View Classifier Errors
Input and Output
Representations
Concept: the rule you want to learn
Instance: one data point from your training or testing data (row in table)
Attribute: one of the features that an instance is composed of (column in table)
Numeric versus Nominal Attributes What kind of reasoning does your
representation enable? Numeric attributes allow instances to be
ordered Numeric attributes allow you to measure
distance between instances Sometimes numeric attributes make too fine
grained of a distinction
.2 .25 .28 .31 .35 .45 .47 .52 .6 .63
Numeric versus Nominal Attributes
.2 .25 .28 .31 .35 .45 .47 .52 .6 .63
Numeric attributes can be discretized into nominal values Then you lose ordering and distance Another option is applying a function that maps a range
of values into a single numeric attribute
Nominal attributes can be mapped into numbers i.e., decide that blue=1 and green=2 But are inferences made based on this valid?
Numeric versus Nominal Attributes
.2 .25 .28 .31 .35 .45 .47 .52 .6 .63
.2 .3 .5 .6
Numeric attributes can be discretized into nominal values Then you lose ordering and distance Another option is applying a function that maps a range
of values into a single numeric attribute
Nominal attributes can be mapped into numbers i.e., decide that blue=1 and green=2 But are inferences made based on this valid?
Example!
Problem: Learn a rule that predicts how much time a person spends doing math problems each day
Attributes: You know gender, age, socio-economic status of parents, chosen field if any
How would you represent age, and why? What would you expect the target rule to look like?
Styles of Learning Classification – learn rules from labeled
instances that allow you to assign new instances to a class
Association – look for relationships between features, not just rules that predict a class from an instance (more general)
Clustering – look for instances that are similar (involves comparisons of multiple features)
Numeric Prediction (regression models)
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
What else would be affected if wheatwere to disappear?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
How would you represent this data?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
What would the learned rule look like?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
What would the learned rule look like?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
Food Web What if you wanted a more general rule: i.e., Affects(Entity1, Entity2)
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
Food Web What if you wanted a more general rule: i.e., Affects(Entity1, Entity2)
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
Food Web What if you wanted a more general rule: i.e., Affects(Entity1, Entity2)
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
122 rows altogether!Now let’s look at the learned rule….
Food Web What if you wanted a more general rule: i.e., Affects(Entity1, Entity2)
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
122 rows altogether!Now let’s look at the learned rule….
Food Web What if you wanted a more general rule: i.e., Affects(Entity1, Entity2)
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
122 rows altogether!Now let’s look at the learned rule…. Does it have to be this complicated?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
What would your representation for Affects(Entity1, Entity2) look like?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
What would your representation for Affects(Entity1, Entity2) look like?
Food Web
http://www.cas.psu.edu/DOCS/WEBCOURSE/WETLAND/WET1/identify.html
What would your representation for Affects(Entity1, Entity2) look like?
More on Cross-Validation
Cross Validation Exercise
What is the same?What is different?
1 2
3 45
What surprises you?
Compare Folds with Tree Trained on Whole Set1 2
3 45
Train Versus TestPerformance on Training Data Performance on Testing Data
Which Model Do You Think Will Perform Best on Test Set?1 2
3 45
Fold 1
Fold 2
Fold 3
Fold 4
Fold 5
Total Performance
What do you notice?
Total Performance
Average Kappa = .5
Starting to think about Error Analyses
Step 1: Look at the confusion matrix Where are most of the errors occurring? What are possible explanations for systematic
errors you see? Are the instances in the confusable classes too similar
to each other? If so, how can we distinguish them? Are we paying attention to the wrong features? Are we missing features that would allow us to see
commonalities within classes that we are missing?
What went wrong on Fold 3?1 2
3 45
What went wrong on Fold 3?
Training Set Performance Testing Set Performance
Hypotheses?
What went wrong on Fold 3?
Training Set Performance Testing Set Performance
Hypotheses?
What’s the difference?
Hypothesis: Problem with first cut
Some Examples
What do you conclude?
What do you conclude?
Problem with Fold 3 was probably just a sampling fluke.Distribution of classes different between train and test.
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