In the modern age where most assets and valuable information are digitalized, phishing scams are an important way criminals steal money, much higher than “normal” physical robbery & extortion, with up ...

In the world of data science and machine learning, building accurate predictive models is not just about having large volumes of data—it is about selecting the right algorithm that can uncover ...

Artificial intelligence is rapidly changing the job market, automating jobs across industries. Therefore, in such a scenario, upskilling oneself in industry-relevant AI skills becomes even more ...

Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...

Abstract: The background of the present study complies with silicon content prediction in hot metal in the blast furnace system. The blast furnace system is a highly complex industrial reactor in the ...

GBRL is a Python-based Gradient Boosting Trees (GBT) library, similar to popular packages such as XGBoost, CatBoost, but specifically designed and optimized for reinforcement learning (RL). GBRL is ...

XGBoost (extreme Gradient Boosting) has revolutionized the way we build predictive models. Its speed, efficiency, and superior performance make it one of the most powerful machine learning algorithms ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the gradient boosting regression technique, where the goal is to predict a single numeric value. Compared to ...

Abstract: Gradient boosting is an efficient and scalable supervised machine learning technique, and most scaling models based on gradient boosting perform well on point regression tasks, but they can ...

Space complexity of machine learning algorithms is the amount of memory or storage an algorithm requires for its successful execution. This becomes one of the important metrics of concern since it ...

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a ...

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree ...