Answer:
a. This is an instance of overfitting.
Explanation:
In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.
Answer:
If something goes wrong, it doesn't affect any real-world situations or the company itself in the real world
Explanation:
<span>B. users vote on the relevance of the source, which affects whether it will appear in future searches. </span>
