Model Evaluation - lesson 7 | HackIndia Skills

Model Evaluation - lesson 7 | HackIndia Skills | HackIndia

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Model Evaluation

Dataset:
Used 2,400 labelled support messages collected from our demo dataset. Labels: refund, delivery, product, other.

Split method:
80/20 random split with a fixed seed. Train: 1,920 rows. Validation: 480 rows.

Baseline:
Majority-class baseline predicts "delivery" for every message. Baseline accuracy: 42%.

Model result:
Validation accuracy: 78%.
Key metric: macro F1 = 0.74, because classes are not equally common.

Confusion matrix:
Rows are actual labels. Columns are predicted labels.

              refund  delivery  product  other
refund            82        11        5      6
delivery          14       151       13     10
product            8        21       72      9
other              6        19        4     49

Limitation:
The model was tested only on English messages. It may perform worse on Hinglish or regional language messages.

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Say the result in plain words during the demo. This sounds more credible than only showing a high score.

We tested the model on a held-out 20% validation split. It beats the majority-class baseline, 78% accuracy versus 42%. The confusion matrix shows it still confuses product and delivery messages, so we would not use it for automatic replies without human review.

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Do not oversell

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