Discover the main cause of overfitting in machine learning – training models on insufficient data. Learn why too little data, not too much, leads to poor generalization.
Table of Contents
Question
When do we have a case of overfitting?
A. By training a model on too much data.
B. By training a model on only numeric data.
C. By training a model on too little data.
Answer
C. By training a model on too little data.
Explanation
Overfitting occurs when a machine learning model is trained on too little data, not too much data. When a model is trained on an insufficiently small dataset, it can essentially “memorize” the training examples and fit the training data too closely, including noise and outliers. This leads the model to perform very well on the training set but generalize poorly to new, unseen data.
The model becomes overly complex and tunes itself to the idiosyncrasies and random fluctuations in the small training set rather than learning the true underlying patterns. It fails to capture the general trends that would enable it to make accurate predictions on novel examples.
In contrast, training on a large, representative dataset helps the model learn the signal and disregard the noise. More data allows the model to identify the important generalizable patterns and avoid getting thrown off by outliers or random variations.
So in summary, overfitting stems from training on too little data, while more data helps combat overfitting and enables the model to generalize well. The key is having a training set that is sufficiently large and representative of the problem space.
Google AI for Anyone certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Google AI for Anyone exam and earn Google AI for Anyone certification.