Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size

PurposeThis study identifies whether the Internet search index can be used as effective enough data to identify agricultural and livestock product demand and compare the accuracy of the prediction of major agricultural and livestock products purchases between these prediction models using artificial neural network, linear regression and a decision tree.Design/methodology/approachArtificial neural network, linear regression and decision tree algorithms were used in this study to compare the accuracy of the prediction of major agricultural and livestock products purchases. The analysis data were studied using 10-fold cross validation.FindingsFirst, the importance of the Internet search index among the 20 explanatory variables was found to be high for most items, so the Internet search index can be used as a variable to explain agricultural and livestock products purchases. Second, as a result of comparing the accuracy of the prediction of six agricultural and livestock purchases using three models, beef was the most predictable, followed by radishes, chicken, Chinese cabbage, garlic and dried peppers, and by model, a decision tree shows the highest accuracy of prediction, followed by linear regression and an artificial neural network.Originality/valueThis study is meaningful in that it analyzes the purchase of agricultural and livestock products using data from actual consumers' purchases of agricultural and livestock products. In addition, the use of data mining techniques and Internet search index in the analysis of agricultural and livestock purchases contributes to improving the accuracy and efficiency of agricultural and livestock purchase predictions.

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Assessment of predictive models for the estimation of heat consumption in kindergartens

Thermal Science ◽

10.2298/tsci201026084j ◽

2021 ◽

pp. 84-84

Author(s):

Nebojsa Jurisevic ◽

Dusan Gordic ◽

Arso Vukicevic

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Decision Tree ◽

Multiple Linear Regression ◽

Predictive Models ◽

Influential Factors ◽

Data Availability ◽

Heat Consumption ◽

Artificial Neural

The service sector remains the only economic sector that has recorded an increase (3.8%) in energy consumption during the last decade, and it is projected to grow more than 50% in the following decades. Among the public buildings, educational are especially important since they have high abundance, great retrofit potential in terms of energy savings and impact in promoting a culture of energy efficiency. Since predictive models have shown high potential in optimizing usage of energy in buildings, this study aimed to assess their application for both finding the most influential factors on heat consumption in public kindergarten and heat consumption prediction. Two linear (Simple and Multiple Linear Regression) and two non-linear (Decision Tree and Artificial Neural Network) predictive models were utilized to estimate monthly heat consumption in 11 public kindergartens in the city of Kragujevac, Serbia. Top-performing and most complex to develop was the Artificial Neural Network predictive model. Contrary to that, Simple Linear Regression was the least precise but the most simple to develop. It was found that Multiple Linear Regression and Decision Tree were relatively simple to develop and interpret, where in particular the Multiple Linear Regression provided relatively satisfying results with a good balance of precision and usability. It was concluded that the selection of proper predictive methods depends on data availability, and technical abilities of those who utilize and create them, often offering the choice between simplicity and precision.

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