Step 1: Understanding the Question:
The question asks to identify the machine learning problem associated with a model that exhibits high variance.
Step 2: Detailed Explanation:
In machine learning, the performance of a model is often analyzed through the lens of the bias-variance tradeoff.
Bias is the error from erroneous assumptions in the learning algorithm. High bias can cause a model to miss relevant relations between features and target outputs. This is known as underfitting. The model is too simple.
Variance is the error from sensitivity to small fluctuations in the training set. High variance means the model pays too much attention to the training data, including its noise and random fluctuations.
A model with high variance performs very well on the data it was trained on but fails to generalize to new, unseen data. This phenomenon, where the model essentially "memorizes" the training data instead of learning the underlying pattern, is called overfitting. The model is too complex for the data.
Step 3: Final Answer:
A high variance in a machine learning model is a primary indicator of overfitting.