Examples of using Classification and regression in English and their translations into Portuguese
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The first paper presents the classification and regression trees, as well as bagging, random forest and boosting algorithms.
Students will also be able to design, train and deploy machine learning models for classification and regression.
The CART(Classification and Regression Tree) identified three strata of homogeneous city groups according to socioeconomic factors.
The minimal learning machine(mlm)is an inductive learning method applied to supervised classification and regression problems.
The mlm has reached a promising performance in various classification and regression problems compared with other classical methods of learning.
Support vector machines(SVMs), also known as support vector networks,are a set of related supervised learning methods used for classification and regression.
His most important contributions were his work on classification and regression trees and ensembles of trees fit to bootstrap samples.
Support Vector Machines(SVM)belong to a family of supervised learning algorithms that can efficiently solve classification and regression problems.
After univariate analyses,a decision tree was developed with the Classification And Regression Tree CART algorithm to identify risk factors for PU development.
In the scientific review is discussed and exemplified the use of decision tree algorithms to generate predictive models of occurrence of soil class,being emphasized the cart algorithm classification and regression tree.
These moments are analyzed using classification and regression trees, and coefficients of localization, with information for the period 2002-2007.
The calibration techniques used were multiple linear regression(mlr), and data mining techniques as the classification and regression tree(cart) and k-means.
Among generated, ad developed with the classification and regression trees algorithm was chosen to be clinically validated by presenting the best overall predictive power 86.4.
First, a hybrid grapheme-to-phoneme converter for brazilian portuguese, named aeiouadô,which makes use of both manual transcription rules and classification and regression trees(cart) to infer the phone transcription.
The Allegheny Decision Tree is a user implementation of the Classification and Regression Tree(CART) and the Chi-Square Automatic Interaction Detection(CHAID) algorithms for predictive modeling available as part of the ParaTree Component Library.
In machine learning, support vector machines(SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns,used for classification and regression analysis.
Finally, this solution was tested by comparing the result of a cart(classification and regression tree) algorithm, in order to verify the effectiveness of svm technique.
Least-squares support-vector machines(LS-SVM) are least-squares versions of support-vector machines(SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
The 2000 data were used for calibration and 2010 data to validate the models with data mining technique(decision tree- dt algorithms:cart- classification and regression tree and chaid- chi-squared automatic interaction detection)and multiple linear regression(mlr) for comparative purposes with the dt models.
In order to use the amount of risk factors as a predictive variable for atrial fibrillation and set a cut-off value to produce the most homogeneous groups ofpatients possible regarding arrhythmia, a multivariate exploratory technique known as classification and regression tree CART analysis was performed.
Regarding the statistical techniques,it is expected that machine learning(especially classification and regression trees), methodologies for controlling spurious associations(such as Bonferroni correctionand false discovery rate) and methodologies for dimension reduction(such as principal components analysis and propensity scores) will be increasingly used.
The two main kinds of tasks for prediction are the classification and the regression.
Fuzzy classification and multivariable regression models have been proposed to evaluate the stabilityand combustion quality of bunker fuel and its blends with biodiesel.
This dissertation proposes parsimonious models for regression and classification tasks in cross-sectional datasets under random sample hypothesis.
Regression and classification algorithms usually expect data to be be fully filled which is not often the case in many of nowadays databases.
Learn to build and evaluate regression, classification and clustering models.
From clustering and regression to classification and probabilistic inference,and to data enrichment and visualization, data scientists need to have a solid foundation in computer science and applications, modeling, statistics, analytics, and mathematics.
Supervised learning and how it can be applied to regression and classification problems.
Apply techniques for data management, including data wrangling, cleaning, sampling, management,exploratory analysis, regression and classification, prediction, and data communication.
In the second block we began to immerse ourselves in machine learning,working on real problems of classification, regression and grouping using structured and unstructured datasets.