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The information gain increases with differentiation between p y|x) and p y).
The information gain IG indicates the difference between the within variance and weighted between variance.
The information gain peaked in small area, and additional observation locations were very well spatially defined.
The information gain to select the sensor can be defined as, where is the new measurement from sensor at time.
The information gain and the random forest both assign a real-number score to each feature, which we used directly.
The information gain corresponds to the expected number of extra bits that are lost if the dataset is neglected.
The information gain measures how much information about the class one gains by knowing the value of a feature.
The information gain for a certain feature is calculated as the difference between the entropy before splitting the training examples with this feature and the entropy after splitting.
Note that the information gain is used for the selection.
Therefore, this is the information gain due to attribute X.
The second term, I(x t ), is the information gain of a state.
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