Applications of the Decision Tree in Business Field 来自 Semantic Scholar 喜欢 0 阅读量: 1 作者: Z Zhang 摘要: With the advent of the information age and the development of the Internet, the data show explosive growth, machine learning is becoming a more and more popular field. This paper ...
In our blog on the applications of Machine Learning, we will start by looking at Google Maps’ traffic prediction. Google Maps’ Traffic Prediction We will start with one of the Machine Learning applications that we use in our day-to-day life, i.e., Google Maps’ traffic prediction. ...
摘要: Top-down algorithms such as C4.5 and CART for constructing decision trees are known to perform boosting, with the procedure of choosing classification rules at internal nodes regarded as the base lear关键词: Economical and environmental implications of solid waste compost applications to ...
IN recent decades, a surge of interest in Machine learning within the medical research community has resulted in an array of successful data-driven applications ranging from medical image processing and the diagnosis of specific diseases, to the broader tasks of decision support and outcome prediction...
Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 2015; 27(2):130-5.Song, Y., L. Ying 2015. Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry 27(2): 130....
Regarding the ML algorithms, we compared five frequently used supervised ML methods—SVM, LR, decision tree (DT), neural network (NN), and Naïve Bayes (NB), to examine the effect of different sample sizes (small to large) on ML performance. We employed ten-fold cross-validation to quant...
In RFA, a large number of decision trees are created, with each decision tree containing a set of rules to differentiate samples within the mass spectral data matrix[25]. A subset of the uncorrelated decision trees is compiled to create a classification model that can be used to classify ...
1. Some of the commonly used ML technologies are linear regression, decision trees, and random forest in which generalized models are trained to learn coefficients/weights/parameters for a given dataset (usually structured i.e., on a grid or a spreadsheet). Applying traditional ML techniques to...
Fig. 11. An illustration of decision tree model to find out whether a person is overweighted. 5.2. Unsupervised learning In unsupervised learning (UL), the input data are unlabeled data, where the algorithm has to find patterns and hidden structures to learn a useful function. The enormous dat...
Decision tree, naive bayes, SVM, neural networks, logistic regression, bagging and boosting methods, linear and non-linear regression, various methods for time series analysis, k-means, density-based clustering, Kohonen maps, factor analysis, and many others. GPU cluster support is planned in ...