Criado por Victor Vergara
aproximadamente 6 anos atrás
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Questão | Responda |
Support vector machine | Supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. |
Random forests or random decision forests | an ensemble learning method for classification, regression, and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. |
Gradient boosting | a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. |
k-means clustering | a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. |
Density-based spatial clustering of applications with noise (DBSCAN | It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). |
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