In the above image, the prediction made at any value of X is shown by the blue line. This predictive model is an overfit for the training data.

Question

In the above image, the prediction made at any value of X is shown by the blue line.  This predictive model is an overfit for the training data.

BlackTom AI · Accepted Answer

Question restatement: The image shows a plot where the blue line represents the model's predictions for any value of X, and the accompanying text says this predictive model is an overfit for the training data. Answer options: ["an overfit"].

Explanation of the single option:
- The core idea of overfitting is that the model captures not only the underlying trend but also the random noise present in the training data. In the plot, the blue prediction line follows the chaotic, noisy gray data points very closely across the entire range of X, including small fluctuations that do not reflect the true underlying relationship. This close alignment with noise indicates the model has learned idiosyncrasies of the training data rather than the true pattern, which is the hallmark of overfitting.

Why this option is correct:
- The statement "an overfit" directly describes the situation where the model is excessively tailored to the training data, as evidenced by the blue line mirroring the training noise. This aligns with the common diagnostic: high variance and low bias, where the model performs poorly on unseen data due to reliance on noise.

Why other choices (if any) would be incorrect:
- If there were an option suggesting the model is underfitting, it would be incorrect here, because underfitting would show a too-simple line that fails to capture the general trend, not a line that tracks the noise. However, in this specific question, only the option "an overfit" is provided, so the discussion about alternatives serves to contrast the concept conceptually.

Additional clarification:
- The visual cue of overfitting in this context is the blue line oscillating with high variance around the scattered gray points and diverging from a smooth, true relationship if the data were to be generated from a simpler underlying function. The absence of a test-set or validation evaluation in the image further supports the interpretation that the model has memorized training data rather than generalized well.

FA25_QTM_110_1 Quiz #11: Prediction, Machine Learning, and the Bias-Variance Tradeoff

In the above image, the prediction made at any value of X is shown by the blue line. This predictive model is an overfit for the training data.

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