Applied Data Science Coding with Python: How to get Classification LogLoss Metric

LogLoss, also known as logarithmic loss, is a performance metric commonly used in classification problems. It measures the difference between predicted probability and the true label. In simple terms, it calculates the error rate between the predicted values and the actual values. The lower the log loss, the better the model is at predicting the true label. In Python, the log loss can be calculated using scikit-learn library by importing the log_loss function from metrics module and passing in the true labels and predicted probabilities. It is important to note that log loss can only be calculated for binary and multiclass classification problems and not for regression problems.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Classification LogLoss Metric.

 

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