摘要: Artificial intelligence and machine learning applications are of significant importance almost in every field
of human life to solve problems or support human experts. However, the determination of the machine
learning model to achieve a superior result for a particular problem within the wide real-life application areas
is still a challenging task for researchers. The success of a model could be affected by several factors such as
dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required
to determine model ability and the efficiency of the considered strategies. This study implemented ten
benchmark machine learning models on seventeen varied datasets. Experiments are performed using four
different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques.
We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute
error, and coefficient of determination (R2 score). The considered models are analyzed, and each model's
advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of
experiments, the deep Long-Short Term Memory (LSTM) neural network outperformed other considered
models, namely, decision tree, linear regression, support vector regression with a linear and radial basis
function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and
deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of
the experiments and should be considered for the model evaluation in regression studies where data mining
or selection is not performed.