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.
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.
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!
Let's find out where your model is struggling.
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.