Search results
Results From The WOW.Com Content Network
Finally classifier is generated by using the previously created set of classifiers on the original dataset , the classification predicted most often by the sub-classifiers is the final classification for i = 1 to m { D' = bootstrap sample from D (sample with replacement) Ci = I(D') } C*(x) = argmax #{i:Ci(x)=y} (most often predicted label y) y∈Y
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
Decision trees are a popular method for various machine learning tasks. Tree learning is almost "an off-the-shelf procedure for data mining", say Hastie et al., "because it is invariant under scaling and various other transformations of feature values, is robust to inclusion of irrelevant features, and produces inspectable models.
In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm , and is typically used in the machine learning and natural language processing domains.
AdaBoost (with decision trees as the weak learners) is often referred to as the best out-of-the-box classifier. [ 4 ] [ 5 ] When used with decision tree learning, information gathered at each stage of the AdaBoost algorithm about the relative 'hardness' of each training sample is fed into the tree-growing algorithm such that later trees tend to ...
Decision tree learning is a method commonly used in data mining. [3] The goal is to create a model that predicts the value of a target variable based on several input variables. A decision tree is a simple representation for classifying examples.
In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. [ 1 ] [ 2 ]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]