AI-900: How Does Insufficient Training Data Impact AI Models? (Key to Avoiding Underfitting)

Learn how limited training data leads to underfitting in AI models, reducing their ability to generalize and perform accurately. Discover solutions to improve model performance.

Question

What problem is caused when you train the model with a limited amount of data?

A. Overfitting
B. Regression
C. Underfitting
D. Local minimization

Answer

C. Underfitting

Explanation

The problem caused when you train a model with a limited amount of data is underfitting. When you train a model with limited data, it restricts the model’s ability to learn the underlying relationships between features and the target variable effectively. This results in:

• Poor performance on both training and unseen data: The model cannot capture the full complexity of the data, leading to inaccurate predictions on both the data it saw during training and new, unseen data.
• High bias: The model is overly simplified and underestimates the complexity of the data, leading to biased predictions that deviate from the true relationship.

Overfitting occurs when a model is too complex and “overfits” the training data, failing to generalize well to unseen data. While limited data can contribute to overfitting if the model is also very complex, it is more likely to lead to underfitting.

Regression is a type of machine learning technique used for predicting continuous target variables based on features. It is not a problem caused by limited data but rather a method that can be used to address certain problems.

Local minimization refers to the situation where an optimization algorithm gets stuck in a local minimum, which is not the global minimum of a loss function. While limited data can increase the chances of getting stuck in local minima, it does not describe the problem of underfitting.

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