Lesson 29: Project Workshop — Interpretation & Presentation
Interpret model predictions; visualize results effectively.
Project Workshop: Interpretation & Presentation
A great model is useless if you can't explain how it works! In this final project step, we will interpret our model's predictions and visualize the results.
Feature Importance
Which words strongly indicate a positive review? Which indicate a negative review? By looking at the coefficients of our Logistic Regression model or the feature importances of our Random Forest, we can extract the most predictive words.
Error Analysis
No model is perfect. It's crucial to look at where your model failed. By examining the false positives and false negatives, you can often find clues on how to improve the model. For instance, models often struggle with sarcasm!
Coding Challenge: Error Analysis
Let's find out where your model is struggling.
- Generate a confusion matrix for your final model's test predictions.
- Find 5 instances of false positives (the model predicted positive, but the actual label was negative).
- Print out the text of those 5 reviews.
- Read them. Can you hypothesize why the model got them wrong? Were they sarcastic? Did they use positive words in a negative context (e.g., "Not a good movie")?
The Final Dashboard
In the real world, you'd present your findings to stakeholders. A great way to do this is a dashboard summarizing the class distribution, confusion matrix, top features, and a model comparison chart.