AI-900: What Is Overfitting in Machine Learning and How Can It Impact Your Model’s Performance?

Learn about overfitting in machine learning, its causes, and its impact on model performance. Discover strategies to prevent overfitting for better AI predictions.

Question

What problem is caused when you use a model with too much flexibility and an excessive number of parameters?

A. Regularization
B. Overfitting
C. Convergence
D. Underfitting

Answer

B. Overfitting

Explanation

The problem caused when you use a model with too much flexibility and an excessive number of parameters is overfitting. When a model has excessive flexibility and too many parameters, it becomes too good at fitting the training data, potentially memorizing even the noise and specific details present in the data. This leads to:

• Poor performance on unseen data: The model fails to generalize well to new data that it has not seen during training.
• High variance: Small changes in the input data can lead to significantly different predictions, indicating that the model is overly sensitive to specific details rather than capturing the underlying relationships.

Underfitting occurs when a model is too simple and fails to capture the underlying relationships in the data. While using a model with too few parameters can contribute to underfitting, excessive flexibility and parameters typically lead to the opposite problem – overfitting.

Regularization is a technique used to prevent overfitting by penalizing models for having too much complexity or too many parameters. It is the solution to the problem, not the problem itself.

Convergence refers to the process of a machine learning model reaching a stable state where its performance on the training data stops improving significantly. Convergence is not directly related to the issue of using a model with too much flexibility and parameters.

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