In Machine Learning, classification problem refers to predictive modeling where a class label needs to be predicted for a given observation (record). While the input data (features) comprise of either continuous or categorical variables, the output is always a categorical variable. For example, based on input features such as weather information (humidity, temperature, cloudy/sunny, wind speed, etc.) and time of year, predict whether it is going to “rain” or “not rain” (output variable) today in your city. Another example, based on email’s content and sender information, predict whether it is “spam” or “not spam” (aka “ham”).

Log-loss is one of the major metrics to assess the performance of a classification problem. But what does it conceptually mean? When you google the term, you easily get good articles and blogs that directly dig into the mathematics involved. That said, I plan to take a different approach here — talk about the intuition behind the metric and then provide the formula used to calculate the metric. …