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Chapter 3: Predictive Modeling (General Info/Terminology (Predictive vs.…
Chapter 3: Predictive Modeling
General Info/Terminology
Attributes vs. Features
Predictive vs. Descriptive Modeling
Forecasting/ Describing real relationships
Supervised vs. Unsupervised
In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the data is clustered into different classes .
Classification vs. Regression
Classification trees have dependent variables that are categorical and unordered. Regression trees have dependent variables that are continuous values or ordered whole values. ... Regression means to predict the output value using training data. Classification means to group the output into a class
Induction vs. Deduction
Deductive = "Top down"
Selecting Informative Attributes
Numeric Variables
Varience
Informative Gain (IG)
Pure vs. Impure
Entropy
Tree Structured Models
Tree Induction
Probability Estimation Tree
Visual Representation
Classification Decision Tree
Tree Induction
Decision Lines and Hyper-planes