Text Classification & Sentiment Analysis using Microsoft Azure & R: Optimizing Lambda for AUC

The choice of the 'Lambda' value depends on the specific requirements and characteristics of the text classification and sentiment analysis task.

A lower value of 'Lambda' (higher value of C) would result in a lower cost penalty and a higher emphasis on fitting the training data. This may lead to overfitting and a lower performance on the test data. On the other hand, a higher value of 'Lambda' (lower value of C) would result in a higher cost penalty and a higher emphasis on generalization. This may lead to underfitting and a lower performance on the training data.

Therefore, the choice of 'Lambda' should be based on a trade-off between overfitting and underfitting, and should be determined through experimentation and validation on a separate test set. In the given scenario, a 'Lambda' value of 0.001 seems to provide the best trade-off between fitting and generalization, as it results in the highest AUC value.