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In decision tree learning, information gain ratio is a ratio of information gain to the intrinsic information. It was proposed by Ross Quinlan, [1] to reduce a bias towards multi-valued attributes by taking the number and size of branches into account when choosing an attribute. [2] Information gain is also known as mutual information. [3]
Although information gain is usually a good measure for deciding the relevance of an attribute, it is not perfect. A notable problem occurs when information gain is applied to attributes that can take on a large number of distinct values. For example, suppose that one is building a decision tree for some data describing the customers of a business.
Discounted cumulative gain (DCG) is a measure of ranking quality in information retrieval. It is often normalized so that it is comparable across queries, giving Normalized DCG (nDCG or NDCG) . NDCG is often used to measure effectiveness of search engine algorithms and related applications.
In a slow-fading channel, where the coherence time is greater than the latency requirement, there is no definite capacity as the maximum rate of reliable communications supported by the channel, (+ | |), depends on the random channel gain | |, which is unknown to the transmitter.
where is the Kullback–Leibler divergence, and is the outer product distribution which assigns probability () to each (,).. Notice, as per property of the Kullback–Leibler divergence, that (;) is equal to zero precisely when the joint distribution coincides with the product of the marginals, i.e. when and are independent (and hence observing tells you nothing about ).
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This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.