COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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When a predictive model is accurate but takes too long to run
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When the model learns specifics of the training data that can’t be generalized to a larger data set
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When you perform hyperparameter tuning and performance degrades
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When you apply a powerful deep learning algorithm to a simple machine learning problem
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Detailed explanation-1: -This chapter introduced the process of tuning model hyperparameters that cannot be directly estimated from the data. Tuning such parameters can lead to overfitting, often by allowing a model to grow overly complex, so using resampled data sets together with appropriate metrics for evaluation is important.
Detailed explanation-2: -Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data. When data scientists use machine learning models for making predictions, they first train the model on a known data set.
Detailed explanation-3: -Overfitting is a concept in data science, which occurs when a statistical model fits exactly against its training data.
Detailed explanation-4: -Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.