Please enable JavaScript.
Coggle requires JavaScript to display documents.
Provost - Chapter 3 (Definitions (Information (Is a quantity that reduces…
Provost - Chapter 3
Definitions
-
Supervised Learning
Model creation where the model describes a relationship between
a set of selected variables (attributes or features) and a predefined variable
-
Instance / example
-
Also called feature vector because it can be represented as a fixed-length ordered collected of feature values
-
Deduction
Starts with general rules and specific facts, and creates other specific facts from them.
-
Supervised Segmentation
-
-
-
Tree Induction
Starts with the whole dataset & applies variable selection to try creat the "purest" subgroups possible using the attributes.
-
-
-
Probability Estimation
Frequency-based estimate
If a leaf contains n positive instances and m negative instances, the probability of any new instance being positive may be estimated as n/(n+m)
Overfitting
Being overly optimistic about the probability of class membership for segments with very small numbers of instances
-
Tree Induction
-
Incorporates the idea of supervised segmentation in an elegant manner, repeatedly selecting informative attributes
Models, Induction & Prediction
Descriptive Model
-
Is Judged in part on its intelligibility, and a less accurate model may be preferred if it is easier to understand
Prediction Model
Estimate an unknown value (Could be in the future, present or past)
-
-