[PDF] Top 20 Predicting oenological attributes using machine learning models

... when predicting for the TF 2014 dataset than for the TF 2013 set of samples, with very similar error measures, which might indicate that the model has difficulties in capturing the relationships between the ... See full document

... To complete our feature analysis we can also see the results of the built in average MSE feature ranking and node purity by the randomforest package on Figure 4.2 . These results indicate the The full importance results ... See full document

... addressed using data mining ...produce models that are intelligible to experts, such as J48 and Ridor. Using J48 we manage to produce a small size decision tree (shown in Figure 3) that uses very ... See full document

... supervised learning, to accurately classify which start-ups are successful and which ...thus using a low number of observations compared with the present ...complex machine learning algorithms ... See full document

... in predicting the importation of dengue at an aggregated level for ...dynamic attributes of the different country ...consideration. Using the local interpretable model-agnostic explanations, we were ... See full document

... proposed models apply systems biology approaches, such as protein -protein interaction networks and pathway analysis to study drug respon ...or using “omics” data, such as genomic data, and some of these ... See full document

... Predicting stock market prices is far from being a trivial task. The uncertainty and volatility that characterize stock markets makes very hard and sometimes even impossible to predict what will happen. ... See full document

... The goal of our study is to evaluate if and how rankings add value to investors. With this purpose, we develop several sets of active trading strategies, selecting the stocks most favored by analysts. The first strategy ... See full document

... Accuracy using adjusted four factors The main conclusions were that MLP and Naïve Bayes gave consistently best results and more training data does not translate into better ...good models relies on the ... See full document

... ML models, with rows corresponding to each article and columns to each ...results using data from all types of biomarkers of exposure (dietary, pollutant and reproducibility values) were not very high given ... See full document

... several machine learning techniques for short-term water demand forecast such as KNN, SVR, Random Forest Regression and ...(LBFGS) learning algorithm and the Rectified Linear Unit (ReLU) activation ... See full document

... Typically, the classic approach to this research topic has been made via using classical models that test hypothesis based on new or past existing theories constructed on grounded literary work. ... See full document

... injuries, machine learning algorithms and data mining methodology are a particularly good ...fit. Machine learning in sports injuries has been used for diagnosing, where Bayesian classifiers ... See full document

... wind models. Arti- ficial neural networks are machine learning classifiers which map a set of input attributes into a boolean or multivalued output attribute ...crossovering attributes ... See full document

... power-law function of the volume of the transaction. [3] used a logarithm function of the transac- tion volume to estimate market impact costs. [4] exploited the hyperbolic tangent function for the same task. [5] and [6] ... See full document

... the attributes, not only of soils, but also other elements of the physical environment (DAVIES; GAMM, 1969; KISS et ...of models where the trend surfaces are fairly simplified and “artificial” ... See full document

... problem using machine learning to both forecast water demands and simulate the consequent behaviour of the network which enables the optimisation of the energy ...conducted using data from ... See full document

... “The holdout method is the simplest kind of cross validation. The data set is separated into two sets, called the training set and the testing set. The function approximator fits a function using the training set ... See full document

... In several real-world applications this assumption may be not true and classifiers built on these unbalanced data may thus produce unsat- isfactory results. When an application is characterized by unbalanced data, two ... See full document

... de Machine Learning, nomeadamente Redes Neuronais Artificiais, na previsão da taxa de inflação em ...de Machine Learning têm sido cada vez mais aplicados à previsão econométrica de índices ... See full document