Question

A high variance in a machine learning model is a primary indicator of which problem?

• A. Underfitting
• B. Overfitting
• C. High bias
• D. Data normalization

Hint:

{High Bias} → Underfitting (model too simple)
{High Variance} → Overfitting (model too complex)

Answer 1

The Correct Option is B

Concept: In machine learning, bias and variance are two sources of error that affect model performance.

• Bias: Error due to overly simple assumptions in the learning algorithm.
• Variance: Error caused by the model being too sensitive to the training data.

A model with high variance learns the training data very well but fails to generalize to new, unseen data.
Step 1: Understand high variance.
High variance occurs when the model captures not only the underlying patterns but also the noise in the training data. This causes the model to perform:

• Very well on training data
• Poorly on testing or new data

Step 2: Identify the associated problem.
This situation is known as overfitting, where the model becomes too complex and fits the training dataset too closely.
Step 3: Conclusion.
Therefore, a machine learning model with high variance typically indicates: \[ \text{Overfitting} \]