Predicting CTRs on Criteo's display ads – Experiments with Machine Learning

Before we dive into exploring and building various models to achieve our objective, we must zero in on a quality metric that'll help us compare them. The most natural choice for a quality metric in the case of a classification problem seems to be that of the 0–1 classification error/accuracy, i.e., the percentage of instances where our model predicted an incorrect/correct label. In our case, the labels would be click and no-click. The alternative is to either use the area under the ROC curve (AUC) or the log-loss as the quality metric. Since the official metric as recommended on the Kaggle's website for this dataset is log-loss, we're going to use the same for the scope of our analysis.