136 lines
3.7 KiB
Markdown
Executable File
136 lines
3.7 KiB
Markdown
Executable File
# Attributes
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- Each example described by fixed pre-defined set of features (attributes)
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- Number of attributes may vary
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- ex. Transportation Vehicles
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- no. wheels not applicable to ships
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- no. masts not applicable to cars
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- Possible solution: "irrelevant value" flag
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- Attributes may be dependent on other attributes
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# Taxonomy of Data Types
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# Nominal Attributes
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- Distinct symbols
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- Serve as labels or names
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- No relation implied among nominal values
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- Only equality tests can be performed
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- ex. outlook = sunny
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# Sources of Missing Values
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- Malfunctioning / Misconfigured Equipment
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- Changes in design
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- Collation of different datasets
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- Data not collected for mining
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- Errors and omissions dont affect purpose of data
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- ex. Banks do not need to know age in banking datasets, DOB may contain missing values
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- Missing value may have significance
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- ex. medical diagnoses can be made from tests a doctor decides, rather than the outcome.
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- Most DM algos assume this is not the case, hence "missing" may need to be coded as an additional nominal value.
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# Inaccurate Values
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- Typographical errors in nominal attributes
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- Typographical and measurement errors in numeric attributes
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- Deliberate errors
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- ex. Incorrect ZIP codes, unsanitised inputs
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- Duplicate examples
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# Weka and ARFF
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## Weather Dataset in ARFF
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### Getting to Know the Data
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- First task, get to know data
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- Simple visualisations useful:
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- Nominal: bar graph
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- Numeric: histograms
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- 2D and 3D plots show dependencies
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- Need to consult experts
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- Too much data? Take sample.
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# Concept Descriptions
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- Output of DM algorithm
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- Many ways of representing:
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- Decision Trees
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- Rules
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- Linear Regression Functions
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## Decision Trees
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- Divide-and-Conquer approach
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- Trees drawn upside down
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- Node at top is root
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- Edges are branches
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- Rectangles represent leaves
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- Leaves assign classification
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- Nodes involve testing attribute
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### Decision Tree with Nominal Attributes
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- Number of branches usually equal to number values
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- Attribute not tested more than once.
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### Decision Tree with Numeric Attributes
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- Test whether value is greater or less than constant
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- Attribute may be tested multiple times
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### Decision Trees with Missing Values
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- Not clear which branch should be taken when node tests attribute with missing value
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- Does absence of a value have significance?
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- Yes => Treat as separate value during training
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- No => Treat in special way during testing
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- Assign sample to most popular branch
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# Classification Rules
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- Popular alternative to decision tree
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- Antecedent (pre-condition) - series of tests
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- Tests usually logically ANDed together
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- Consequent (conclusion) - usually a class
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- Individual rules often logically ORed together
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## If-Then Rules for Contact Lenses
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# Nuggets
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- Are rules independent
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- Problem: Ignores process of executing rules
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- Ordered set (decision list)
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- Order important for interpretation
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- Unordered set
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- Rules may overlap and lead to different conclusions for the same example
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- Needs conflict resolution
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## Executing Rules
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- What if $\geq$ 2 rules conflict?
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- Give no conclusion?
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- Go with the rule that covers largest no. training samples?
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- What is no rule applies to test example?
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- Give no conclusion?
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- Go with class that is most frequent?
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## Special Case: Boolean Classes
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- Assumption: if example does not belong to class "yes", belongs to "no"
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- Solution: only learn rules for class "yes", use default rule for "no"
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- Order is important, no conflicts.
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