In machine learning, model evaluation can determine the efficacy and reliability of predictive systems. Whether it’s discerning fraudulent transactions, diagnosing diseases, or filtering spam emails, the ability to assess the performance of classification models accurately is important. Among the evaluation metrics available, the Area Under the ROC Curve (AUC) is one of the most widely … Read more
In artificial intelligence and machine learning, developing AI systems and ML models comes with its challenges, one of the most common problems being the presence of bias. Whether it’s racial bias in facial recognition algorithms or algorithmic biases in predictive policing systems, the consequences of biased AI can have far-reaching negative impacts. In this article, … Read more
In machine learning, the learning rate is an important parameter that can highly influence the training process and the performance of models. Often described as the “step size” of the optimization process, the learning rate determines the magnitude of updates applied to the model’s weights during training epochs. The choice of learning rate can directly … Read more
