AI-900: What Causes Overfitting in Supervised Machine Learning and How to Avoid It?

Learn the main factors behind overfitting in supervised machine learning, why it occurs, and effective strategies to prevent it for better model performance.

Question

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

A. Using different subsets of labeled data for training and evaluation.
B. Limiting the number of features used in the model.
C. Minimizing the amount of labeled data used for training the model.
D. Using all of the available data to fit a function that relates features and the label.

Answer

D. Using all of the available data to fit a function that relates features and the label.

Explanation

The approach in supervised machine learning that can lead to the problem of overfitting is using all of the available data to fit a function that relates the features and the label. Overfitting occurs when a model memorizes the specific details and noise present in the training data rather than learning the underlying generalizable patterns. Using all of the available data for training increases the risk of the model fitting to these specific details and failing to generalize well to unseen data.

Using different subsets of labeled data for training and evaluation does not lead to the problem of overfitting. This is, in fact, a recommended practice to prevent overfitting. Splitting the data into separate training and evaluation sets ensures that the model is evaluated on unseen data, helping to identify and avoid overfitting.

Minimizing the amount of labeled data used for training the model does not lead to the problem of overfitting. While insufficient data can lead to underfitting (the inability to learn complex relationships), it does not directly cause overfitting.

Limiting the number of features used in the model does not lead to the problem of overfitting. This can be a strategy to address overfitting, but it is not the only approach. Limiting features might also lead to underfitting if important factors are excluded.

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