Search results
Results From The WOW.Com Content Network
For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of the trees. [ 1 ] [ 2 ] Random forests correct for decision trees' habit of overfitting to their training set .
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.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Some different ensemble learning approaches based on artificial neural networks, [51] kernel principal component analysis (KPCA), [52] decision trees with boosting, [53] random forest [50] [54] and automatic design of multiple classifier systems, [55] are proposed to efficiently identify land cover objects.
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 ...
Binary classification is the task of classifying the elements of a set into one of two groups (each called class). Typical binary classification problems include: Medical testing to determine if a patient has a certain disease or not; Quality control in industry, deciding whether a specification has been met;
Classifier chains is a machine learning method for problem transformation in multi-label classification. It combines the computational efficiency of the binary relevance method while still being able to take the label dependencies into account for classification .
Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ลท: y ^ = f ( x ) {\displaystyle {\hat {y}}=f(x)} The samples come from some set X (e.g., the set of all documents , or the set of all images ), while the class labels form a finite set Y defined prior to training.