AI-900: How Does Labeled Data Impact Underfitting in Machine Learning Models?

Explore the key causes of underfitting in supervised machine learning, including the role of labeled data and training approaches. Learn actionable strategies to optimize model performance and avoid common pitfalls.

Question

Which of the following approaches in supervised machine learning can lead to the problem known as “underfitting”?

A. Minimizing the amount of labeled data used for training the model
B. Using models with excessive flexibility
C. Using too many parameters in the model
D. Using all of the available data to fit a function that relates features and the label

Answer

A. Minimizing the amount of labeled data used for training the model

Explanation

The approach in supervised machine learning that can lead to the problem known as “underfitting” is minimizing the amount of labeled data used for training the model. Underfitting occurs when a model is too simple and fails to capture the underlying relationships between features and the target variable accurately. This results in poor performance on both the training and the unseen data. Limiting the amount of labeled data available for training restricts the model’s ability to learn the necessary patterns and complexities present in the data. With insufficient data, the model might not have enough examples to effectively identify the relationships between the features and the target variable, leading to underfitting.

Using all of the available data to fit a function that relates the features, and the label does not lead to underfitting. This approach, while not ideal, can lead to overfitting if the model is too complex for the given data.

Using models with excessive flexibility does not lead to underfitting. Overly flexible models can learn complex patterns, but if there’s not enough data to support these complexities, they can overfit the training data and fail to generalize to unseen data.

Using too many parameters in the model does not lead to underfitting. Including parameters that are irrelevant or redundant can negatively impact the performance of a model. It can increase the model’s complexity and require more data to avoid overfitting.

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