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AI & Data Mining/Exam Revision/Acronyms.md

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+# 1R: (AV)CTARs
+(foreach) Attribute
+	(foreach) Value
+		Count (class)
+		Top (most frequent class)
+		Assign (rule to class)
+	Error Rate (in rule)
+Smallest (error rate)
+
+# Missing Values: Ma $\Delta$D CPS
+Malfunctioning Equipment
+Change in Design
+Collation of Datasets
+Data not collected for mining (Purpose)
+Missing Value is Significant

AI & Data Mining/Exam Revision/Classification.md

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+# 1R
+### (AV)CTARs
+Simple
+Classification
+One-Level tree
+
+Tie, make arbitrary choice
+## Issue with Numerics
+- Discretise
+	- List values of attribute
+	- Sort asc
+	- Write class under each value
+	- Breakpoints between change in class
+	- Interval assigned to majority class
+	- Enforce bucket size, if adjacent interval has same class, merge.
+## Issue with Missing Values
+- Assign "missing" as a value
+- Treat normally
+## Issue with Overfitting
+- Bucket Enforcement
+- Ensure attribute tested is applicable
+
+# PRISM