Generative AI Certificate Q&A: In machine learning, when data model performs exceptionally well during training set phase, but lacks complexity to generate accurate predictions

Question

In machine learning, when a data model performs exceptionally well during the training set phase, but lacks the complexity to generate accurate predictions during the test set phase, the model is _____ the data.

A. reinforcing
B. adversarial
C. overfitting
D. underfitting

Answer

C. overfitting

Explanation

The correct answer is C. overfitting.

When a machine learning model performs exceptionally well during the training set phase but lacks the complexity to generate accurate predictions during the test set phase, it is said to be overfitting the data. Overfitting occurs when a model becomes too specialized or too closely fits the training data, to the extent that it fails to generalize well to unseen data.

During the training phase, the model learns the underlying patterns and relationships present in the training data. However, if the model is too complex or flexible, it can start to learn noise or random variations in the training data, which may not be present in the underlying population or true data distribution.

As a result of overfitting, the model becomes overly specialized to the idiosyncrasies of the training data and fails to capture the general patterns that should apply to unseen data. This lack of generalization leads to poor performance on the test set or real-world data, even though the model may have achieved high accuracy or low error on the training set.

Overfitting can be understood as the model “memorizing” the training data rather than “learning” from it. It may excessively rely on specific data points or noise, leading to a highly specific representation of the training data but lacking the ability to make accurate predictions on new, unseen data.

To address overfitting, various techniques can be applied, such as:

• Regularization: This involves introducing additional terms or penalties in the model’s loss function to discourage overly complex or flexible models. Common regularization techniques include L1 and L2 regularization, which add a penalty term based on the magnitude of the model’s weights.
• Cross-validation: By splitting the available data into multiple subsets, such as a training set, validation set, and test set, cross-validation helps evaluate the model’s performance on unseen data. It provides a more robust estimate of how well the model will generalize.
• Feature selection/reduction: If the model has too many features, it may be prone to overfitting. Removing irrelevant or redundant features can help simplify the model and improve its generalization ability.
• Increasing training data: Providing more diverse and representative training data can help the model learn a better representation of the underlying patterns, reducing the likelihood of overfitting.

By addressing overfitting, a machine learning model can strike a balance between capturing the relevant patterns in the training data while still generalizing well to unseen data, leading to more accurate predictions.

Reference

• ML | Underfitting and Overfitting – GeeksforGeeks
• Overfitting and Underfitting With Machine Learning Algorithms – MachineLearningMastery.com
• Underfitting and Overfitting in Machine Learning | Baeldung on Computer Science
• Overfitting vs. Underfitting: What Is the Difference? | 365 Data Science
• Overfitting – Wikipedia
• What is Overfitting? | IBM
• How to Identify Overfitting Machine Learning Models in Scikit-Learn – MachineLearningMastery.com
• How to Train to the Test Set in Machine Learning – MachineLearningMastery.com
• What is Overfitting? – Overfitting in Machine Learning Explained – AWS (amazon.com)

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