What is the relationship between sample size and overfitting in machine learning?

What is the relationship between sample size and overfitting in machine learning?

In machine learning, overfitting occurs when a model learns the training data too well, including its noise and outliers. A small sample size can lead to overfitting because the model may not generalize well to unseen data.

How does increasing the sample size help prevent overfitting?

Increasing the sample size provides more diverse data points, which helps the model learn the underlying patterns rather than memorizing the training data. This improves generalization to new data.

Is there a specific sample size that is considered optimal?

There isn't a one-size-fits-all optimal sample size; it depends on the complexity of the model and the variability of the data. However, larger sample sizes generally lead to better model performance.

What techniques can I use to reduce overfitting?

You can use techniques like cross-validation, regularization, pruning, and dropout to reduce overfitting. Additionally, increasing your sample size can also help.