A Short Tour of the Predictive Modeling Process

Kühn, Max; Johnson, Kjell

doi:10.1007/978-1-4614-6849-3_2

Cited by 70 publications

(93 citation statements)

References 215 publications

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“…Cubist regression is a rule-based method that was created relevant to the incorporation of the Quinlan opinion. CB is presently a more commonly applied regression and classification method because it was carried in R by Kuhn et al [ 49 ] in 2013. Conceptually, the Cubist regression method is the tree that expands, and the endpoint leaf entails a linear regression model for modeling.…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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“…It should be noted that these algorithms are parameterized, and the choice of their tuning parameters, also referred to as hyperparameters, can significantly impact their performance [ 28 , 29 ]. Table 3 lists the machine learning algorithms deployed in this study along with their corresponding hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

“…From a modeling perspective, these will be 3-level classification tasks and are examined using the classification algorithms shown in Table 2 . In order to avoid overfitting, the data was divided into 2 mutually exclusive subsets: the training set (80%) and the test set (20%) [ 28 , 29 ]. A methodology introduced in [ 29 ] was used for this purpose, with the training and test sets being determined at random but possessing similar distributions of the dependent variable in both sets.…”

Section: Methodsmentioning

confidence: 99%

Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach

Razavi¹,

Xue²

2023

J Med Internet Res

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Background Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional content of food items and have been identified as a potential tool for improving diets. However, due to governmental regulations and the physical limitations of the labels, food labels often lack comprehensive information about the vitamins and minerals present in foods. As a result, information about most of the micronutrients is absent from existing food labels. Objective This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients such as vitamin A (retinol), vitamin C, vitamin B1 (thiamin), vitamin B2 (riboflavin), vitamin B3 (niacin), vitamin B6, vitamin B12, vitamin E (alpha-tocopherol), vitamin K, and minerals such as magnesium, zinc, phosphorus, selenium, manganese, and copper from nutrition information provided on existing food labels. If unreported micronutrients can be predicted with acceptable accuracies from existing food labels using machine learning predictive models, such models can be integrated into mobile apps to provide consumers with additional micronutrient information about foods and help them make more informed diet decisions. Methods Data from the Food and Nutrient Database for Dietary Studies (FNDDS) data set, representing a total of 5624 foods, were used to train a diverse set of machine learning classification and regression algorithms to predict unreported vitamins and minerals from existing food label data. For each model, hyperparameters were adjusted, and the models were evaluated using repeated cross-validation to ensure that the reported results were not subject to overfitting. Results According to the results, while predicting the exact quantity of vitamins and minerals is shown to be challenging, with regression R2 varying in a wide range from 0.28 (for magnesium) to 0.92 (for manganese), the classification models can accurately predict the category (“low,” “medium,” or “high”) level of all minerals and vitamins with accuracies exceeding 0.80. The highest classification accuracies for specific micronutrients are achieved for vitamin B12 (0.94) and phosphorus (0.94), while the lowest are for vitamin E (0.81) and selenium (0.83). Conclusions This study demonstrates the feasibility of predicting unreported micronutrients from existing food labels using machine learning algorithms. The results show that the approach has the potential to significantly improve consumer knowledge about the micronutrient content of the foods they consume. Integrating these predictive models into mobile apps can enhance their accessibility and engagement with consumers. The implications of this research for public health are noteworthy, underscoring the potential of technology to augment consumers’ understanding of the micronutrient content of their diets while also facilitating the tracking of food intake and providing personalized recommendations based on the micronutrient content and individual preferences.

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“…If the chosen model appears to be appropriate, it is utilized for prediction. If the model validation reveals any flaws in the chosen model, the modeling process is repeated to choose a better model (Kuhn and Johnson, 2013).…”

Section: Development Of An Effective Prediction Modelmentioning

confidence: 99%

Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

al.¹

2021

Int. j. adv. appl. sci.

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For businesses and organizations that aim to be efficient and competitive on a worldwide basis, food quality assurance is extremely important. To maintain constant quality, global markets demand high food hygiene and safety standards. Intelligent software to assure fish quality is uncommon in the fishing industry. Most seafood processing industries utilize Total Quality Management (TQM) systems to ensure product safety and quality. These protections ensure that significant quality risks are kept within acceptable tolerance limits. However, there are no ways for calculating the success rates of seafood obtained from different catching centers. The purpose of this study is to develop algorithms for predicting the success rates of seafood caught at different catching centers. To determine the best model to match the data, the algorithms employ the Least-Square Curve Fitting approach. The success rates are predicted using the best-fit model that results. The bestFitModelFinder algorithm is used to find the best model for the input data, while the prediction of quality algorithm is used to predict the success rate. The algorithms were tested using data obtained from a seafood company between January 2000 and December 2019. Statistical metrics such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the presented algorithms. The algorithms' performance analysis resulted in lower error levels. The proposed algorithms can assist seafood enterprises in determining the quality of seafood items sourced from various fishing areas.

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A Short Tour of the Predictive Modeling Process

Cited by 70 publications

References 215 publications

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach

Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

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