Stock price forecasting has been reported as a challenging task in the scientific and financial communities due to stock prices’ nonlinear and dynamic nature. Machine learning models exhibit capabilities that allow them to handle nonlinear data and be candidate tools for stock price forecasting. In this study, an empirical evaluation of eight conventional machine learning models’ is conducted to forecast the stock price of eleven companies belonging to the Saudi Stock Exchange. Moreover, the optimal configuration of hyperparameters in each machine learning model is identified. Forecasting performance is evaluated by two well-known error metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Wilcoxson effect size is utilized to determine the impact of hyperparameter tuning by comparing tuned and un-tuned machine learning models’ forecasting performance. Empirical results indicate there are varying impacts of hyperparameter tuning of machine learning models in forecasting stock price. After tuning the hyperparameters, Support Vector Regression outperforms other forecasting models with a significant statistical difference. In contrast, Kernel Ridge Regression shows noteworthy forecasting performance without hyperparameter tuning with respect to other un-tuned forecasting models. However, Decision Tree and K-Nearest Neighbour are the poor-performing models which demonstrate inadequate forecasting performance even after hyperparameter tuning.

Software defect prediction is an active research area in software engineering. Accurate prediction of software defects assists software engineers in guiding software quality assurance activities. In machine learning, ensemble learning has been proven to improve the prediction performance over individual machine learning models. Recently, many Tree-based ensembles have been proposed in the literature, and their prediction capabilities were not investigated in defect prediction. In this paper, we will empirically investigate the prediction performance of seven Tree-based ensembles in defect prediction. Two ensembles are classified as bagging ensembles: Random Forest and Extra Trees, while the other five ensembles are boosting ensembles: Ada boost, Gradient Boosting, Hist Gradient Boosting, XGBoost and CatBoost. The study utilized 11 publicly available MDP NASA software defect datasets. Empirical results indicate the superiority of Tree-based bagging ensembles: Random Forest and Extra Trees ensembles over other Tree-based boosting ensembles. However, none of the investigated Tree-based ensembles was significantly lower than individual decision trees in prediction performance. Finally, Adaboost ensemble was the worst performing ensemble among all Tree-based ensembles.

Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.