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A company deploys an AI model to predict equipment failure. The model performs well on historical data but fails to generalize to new data from a different factory. Which concept best describes this issue?
Option C (Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patte…Read full explanation
A data scientist trains a linear regression model to predict house prices. The model has high bias and low variance. Which action would most likely reduce bias?
High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding polynomial features increases model complexity by introducing non-linear terms, which allows the linear regression model to better fit the training data and…Read full explanation
An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?
The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing o…Read full explanation
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