Ad Click Prediction: A Comparative Evaluation of Logistic Regression and Performance Metrics

Abstract: This research paper investigates the effectiveness of logistic regression as a predictive modelling technique for ad click prediction. The study aims to explore the performance of logistic regression and evaluate its predictive power using various evaluation metrics. The primary objective is to assess the accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) of logistic regression models in predicting ad clicks. The paper begins with a comprehensive review of the literature on ad click prediction and logistic regression. It highlights the importance of accurate click-through rate (CTR) estimation for advertisers and the potential benefits of logistic regression in this context. The theoretical background of logistic regression is also discussed, providing an understanding of its underlying principles and assumptions. Next, the methodology section describes the dataset used for the study, which includes historical ad impressions and click data. The data pre-processing steps, including feature selection and transformation, are explained. Logistic regression models are then trained on the pre-processed data, and the model performance is evaluated using various evaluation metrics. The results section presents the findings of the study. It includes a detailed analysis of the accuracy, precision, recall, F1 score, and AUC-ROC obtained from the logistic regression models. The performance of the models is compared against benchmark models or alternative algorithms commonly used in ad click prediction. The results highlight the strengths and limitations of logistic regression in predicting ad clicks. Furthermore, the discussion section provides insights into the implications of the results. It discusses the interpretability of logistic regression models and their potential for providing actionable insights to advertisers. The limitations and potential challenges of logistic regression in ad click prediction are also addressed. Finally, the conclusion section summarizes the key findings of the research and provides recommendations for future studies. It emphasizes the significance of logistic regression as a reliable and interpretable method for ad click prediction, while also recognizing the need for further research to improve its performance.