Practical Data Science using Python - Naive Bayes - Model Building and Optimization

The video tutorial covers the process of building a predictive model using the Gaussian Naive Bayes algorithm. It begins with importing necessary libraries and splitting data into training and testing sets. The tutorial explains the importance of using a random state for reproducibility. It then demonstrates creating and training the model, followed by evaluating its performance using a confusion matrix. The model is tested on unseen data to check its generalization capability. The tutorial concludes with a discussion on further investigations using other performance metrics.

What are the implications of a model having a high accuracy score?

Discuss the importance of evaluating a model with both training and test data.

What are some potential issues that can arise from a model with high accuracy but poor predictions?

How can precision and recall provide additional insights beyond accuracy in model evaluation?