How do you explain log loss?

Log-loss is indicative of how close the prediction probability is to the corresponding actual/true value (0 or 1 in case of binary classification). The more the predicted probability diverges from the actual value, the higher is the log-loss value.

What’s a good log loss?

In the case of the LogLoss metric, one usual “well-known” metric is to say that 0.693 is the non-informative value. This figure is obtained by predicting p = 0.5 for any class of a binary problem.

Is log loss a loss function?

Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in Kaggle competitions.

How do you evaluate log losses?

Log loss (i.e. cross-entropy loss) evaluates the performance by comparing the actual class labels and the predicted probabilities. The comparison is quantified using cross-entropy. Cross-entropy quantifies the comparison of two probability distributions.

What is log loss in decision tree?

Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true .

Can log loss have negative values?

3 Answers. Yes, this is supposed to happen. It is not a ‘bug’ as others have suggested. The actual log loss is simply the positive version of the number you’re getting.

How would you explain loss function?

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some “cost” associated with the event.

What does loss function do?

The loss function is the function that computes the distance between the current output of the algorithm and the expected output. It’s a method to evaluate how your algorithm models the data. It can be categorized into two groups.

What is the log loss function?

The Alation State of Data Culture Report! Discover the link between organizations with top-tier data cultures and revenue growth. The log loss function is simply the objective function to minimize, in order to fit a log linear probability model to a set of binary labeled examples.

How do you calculate the log of cross entropy loss?

Binary Cross-Entropy / Log Loss where y is the label (1 for green points and 0 for red points) and p (y) is the predicted probability of the point being green for all N points. Reading this formula, it tells you that, for each green point (y=1), it adds log (p (y)) to the loss, that is, the log probability of it being green.

What is the relationship between the logloss and the uncertainty?

This would now intuitively mean, smaller the value, better is the model i.e. smaller the logloss, better is the model i.e. smaller the UNCERTAINTY, better is the model. This was as simple as I could get. Study economics for business with MIT.

What are some good examples of log loss in data science?

Good example of case where log-loss can be usefull is predicting CTR or click probability in on-line advertising: in paper http://static.googleusercontent.com/media/research.google.com/ru//pubs/archive/41159.pdf googler’s use log loss as ctr prediction metric. What tools besides Python, R and SQL are all data scientists expected to know?