Machine learning regression and classification algorithms utilised for strength prediction of OPC/by-product materials improved soils - ScienceDirectMachine learningCementPFAGGBSOPCBayesian regressorLinear regressionArtificial neural networksLogistic regression...
But perhaps the most common, and most important machine learning tasks – especially for beginners – are regression and classification. Let’s look at regression and classification and see how they compare to eachother as machine learning tasks. After we do that, we’ll look at how they’re ...
The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust regression, new exact algorithms, results for robust online scale estimation, and an evolutionary computation algorithm for different estimators in higher dimensions are presen...
An extension to the R tidyverse for automated ML. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data. - jpfitzinger/tidyfit
If you specify 'Method','Bag', then specify the problem type using the Type name-value pair argument, because you can specify 'Bag' for classification and regression problems. For details about ensemble aggregation algorithms and examples, see Ensemble Algorithms and Choose an Applicable Ensemble ...
Classification is the task of predicting a discrete class label. Regression is the task of predicting a continuous quantity. There is some overlap between the algorithms for classification and regression; for example: A classification algorithm may predict a continuous value, but the continuous value ...
Constructed tree can be then used for classification of new observations. The first part of the thesis describes fundamental principles of tree construction, different splitting algorithms and pruning procedures. Second part of the paper answers the questions why should we use or should not use the ...
predict and print. A classification example The Forensic Glass data set was used in Chapter 12 of MASS4 (Venables and Ripley, 2002) to illustrate vari- ous classification algorithms. We use it here to show howrandomforests work: ...
分类和逻辑回归(Classification and logistic regression) http://www.cnblogs.com/czdbest/p/5768467.html 广义线性模型(Generalized Linear Models) http://www.cnblogs.com/czdbest/p/5769326.html 生成学习算法(Generative Learning algorithms) http://www.cnblogs.com/czdbest/p/5771500.html...
The Forest-based and Boosted Classification and Regression tool trains a model based on known values provided as part of a training dataset. The model can then be used to predict unknown values in a dataset that has the same explanatory variables. The tool creates models and generates predictions...