A classifier shows 100% accuracy on the training set but performs poorly on new cases. What is most likely true?

Question

BlackTom AI · Accepted Answer

The question presents a classifier that fits the training data perfectly yet fails on new cases, which typically signals overfitting rather than other issues.
Option 1: 'It is overfitting' — This is the most plausible explanation. A model that memorizes training data tends to have high accuracy on training samples but poor generalization to unseen data, indicating overfitting. This occurs when the model is too complex relative to the amount or diversity of training data.
Option 2: 'It is underfitting' — Underfitting happens when a model is too simple to capture the underlying patterns, resulting in poor performance on both training and test data. Since the training accuracy is 100%, underfitting is clearly not the issue here.
Option 3: 'It is regularized' — Regularization is a technique to reduce overfitting by penalizing complexity. If the model were properly regularized, we would expect better generalization performance rather than this extreme train-only accuracy. The statement as given doesn’t directly indicate that regularization is the problem; rather, the symptom is overfitting, not a failure of regularization.
Option 4: 'It has high bias' — High bias is associated with underfitting, not with perfect training accuracy and poor test performance. A high-bias model would struggle to fit the training data itself, which contradicts the observed 100% training accuracy.
In summary, the observed behavior—perfect training accuracy coupled with poor generalization to new cases—best aligns with overfitting, while the other options describe different, inconsistent failure modes or miss the core symptom.

COMM_V 414 103 2025W1 Concept Check 3 (individual)

A classifier shows 100% accuracy on the training set but performs poorly on new cases. What is most likely true?

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