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Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Noisy data are data with a large amount of additional meaningless information in it called noise. [1] This includes data corruption and the term is often used as a synonym for corrupt data. [1] It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use unstructured text. Noisy ...
Rather than repeating the algorithm m times, implement it exhaustively (i.e. n times, once for each instance) for relatively small n (up to one thousand). Furthermore, rather than finding the single nearest hit and single nearest miss, which may cause redundant and noisy attributes to affect the selection of the nearest neighbors, ReliefF searches for k nearest hits and misses and averages ...
The figures under the leaves show the probability of survival and the percentage of observations in the leaf. Summarizing: Your chances of survival were good if you were (i) a female or (ii) a male at most 9.5 years old with strictly fewer than 3 siblings. Decision tree learning is a method commonly used in data mining. [3]
A whitening transformation or sphering transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1. [1]
The second class of generalizations to multi-dimensional smoothing deals directly with this scale invariance issue using tensor product spline constructions. [ 10 ] [ 11 ] [ 12 ] Such splines have smoothing penalties with multiple smoothing parameters, which is the price that must be paid for not assuming that the same degree of smoothness is ...
Smoothed data with alpha factor = 0.1. In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points higher ...
An evaluation of the variables is a function from a subset of variables to a particular set of values in the corresponding subset of domains. An evaluation v {\displaystyle v} satisfies a constraint t j , R j {\displaystyle \langle t_{j},R_{j}\rangle } if the values assigned to the variables t j {\displaystyle t_{j}} satisfy the relation R j ...