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I decided to look into it by creating two models one good, one bad on a famous Boat And Stream Formula Bankersadda 10 data science dataset called the Titanic dataset. Specifically, I wanted to see what the distributions looked like for a good and poor predictive model. You can find all of Boat And Stream Formula Bankersadda Github the code I used to create these models on my Github site , the modeling itself will not be the focus of this posting. This - combined with the shape of the distributions tells me that the mean may not be the best representation of LogLoss.

If you look at the last column, Median , these values appear to represent the distributions better. It turns out that you can do that. This is a viable way to compare model performance in classification problems, and should be used over techniques like accuracy.

You can segment LogLoss down even further to look at the target and non-target class. This can tell you if the model is succeeding or failing at predicting one of the variable classes. Home Report About. What does LogLoss really measure? Below you can see a representation of predicted and actual probabilities. Formula Below is the formula for LogLoss from wikipedia. Simple right? However, when I learned about it I became curious about two questions: What does the distribution of LogLoss look like?

Why do we take the mean? Is that really the right choice? Experiment 1: What does the distribution of LogLoss look like? General characteristics of the two models. Search Advanced�. Install the app. JavaScript is disabled. For a better experience, please enable JavaScript in your browser before proceeding.

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