Search results
Results from the WOW.Com Content Network
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees.
In some classification problems, when random forest is used to fit models, jackknife estimated variance is defined as: ... Code of Conduct; Developers; Statistics;
Rotation forest – in which every decision tree is trained by first applying principal component analysis (PCA) on a random subset of the input features. [ 13 ] A special case of a decision tree is a decision list , [ 14 ] which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a ...
mlpack is a free, open-source and header-only software library for machine learning and artificial intelligence written in C++, built on top of the Armadillo library and the ensmallen numerical optimization library.
Regression line for 50 random points in a Gaussian distribution around the line y=1.5x+2. ... Random forest; k-NN; Linear regression; ... Code of Conduct; Developers;
The random subspace method has been used for decision trees; when combined with "ordinary" bagging of decision trees, the resulting models are called random forests. [5] It has also been applied to linear classifiers , [ 6 ] support vector machines , [ 7 ] nearest neighbours [ 8 ] [ 9 ] and other types of classifiers.
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
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 ]