MLS-C01 Modeling Practice Question

Exhibit

Refer to the exhibit.

```
{
  "predicted_label": "spam",
  "predicted_probability": 0.95,
  "label": "ham"
}
```

Refer to the exhibit. A data scientist is evaluating a binary classification model for spam detection. The exhibit shows a single prediction instance. What is the model's prediction for this instance?

⚠ Common exam trap

The MLS-C01 exam often tests the distinction between a model's probability output and its final class prediction, leading candidates to mistakenly select the probability value (0.95) as the prediction instead of the class label (Spam).

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Spam

The model's prediction is 'Spam' because the prediction instance shows a probability of 0.95 for the 'Spam' class, which exceeds the typical decision threshold of 0.5 used in binary classification. Since the probability for 'Spam' is higher than for 'Ham' (0.05), the model assigns the instance to the class with the highest probability, which is Spam.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Ham
Why it's wrong here
'Ham' is the actual label, not the prediction.
✗
0.95
Why it's wrong here
0.95 is the probability, not the predicted label.
✗
The model is unsure because probability is not 1.0
Why it's wrong here
The model still outputs a predicted label.
✓
Spam
Why this is correct
The 'predicted_label' is 'spam'.

Go deeper

MLS-C01 Modeling Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question