If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use

This approach would help to improve the performance of the classifier by providing it with more examples of the religious phrases being used in non-toxic ways. This would allow the classifier to better differentiate between toxic and non-toxic comments that reference these religious groups. Additionally, synthetic data is a cost-effective way to improve the performance of an existing model without requiring a significant investment in human resources.

This is a case where machine learning would be terrible, as it would not be 100% accurate and some passengers would not get picked up. A simple algorith works better here, and the question confirms customers will be indicating when they are at the stop so no ML required.

In predictive maintenance, the goal is to identify which machines are likely to fail soon, so that the repair crew can fix them before they break. In this context, it is important to prioritize detection, while also ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure.
Recall is a metric that measures the proportion of actual positive observations that are correctly predicted as such by the model. In this case, recall is a good metric to use because it measures how well the model is able to identify the machines that are likely to fail soon.
Precision is a metric that measures the proportion of positive predictions that are actually true. In this case, precision is also important because it measures how many of the machines that the model predicts will fail soon, actually do fail soon.
By combining these two metrics, you can ensure that your model is able to identify the machines that are likely to fail soon with a high degree of accuracy. In this case, the model with the highest recall where precision is greater than 0.5 will be the best model, as it will have a high ability to identify the machines that are likely to fail soon and also it will have a high degree of accuracy.
Reference:
Recall and Precision
Predictive Maintenance
Metrics for classification