NLP - Sentiment Analysis Advanced
During training of a 5-class sentiment classifier, the model's accuracy remains around 20% and loss does not improve. What is the most likely reason?
During training of a 5-class sentiment classifier, the model's accuracy remains around 20% and loss does not improve. What is the most likely reason?
medium📝 Debug Q7 of 15
Step-by-Step Solution
Solution:
Step 1: Analyze accuracy baseline
With 5 classes, random guessing yields about 20% accuracy.Step 2: Consider causes of random predictions
Incorrect label encoding or mismatched labels can cause the model to learn nothing.Step 3: Rule out other options
Overfitting usually increases training accuracy; low learning rate slows but does not fix accuracy; dataset size alone doesn't cause random predictions.Final Answer:
The model is predicting classes randomly due to incorrect label encoding -> Option DQuick Check:
Accuracy near random chance suggests label or data issues [OK]
Quick Trick: Accuracy near 1/number_of_classes means random predictions [OK]
Common Mistakes:
MISTAKES- Assuming overfitting causes low accuracy
- Blaming dataset size without evidence
- Ignoring label encoding errors
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