@datasciencefoundry: Naive Bayes is a simple but powerful classification algorithm built on probability 🧠 Here's the intuition: 🔹 The model starts with the probability of each class (the prior). 🔹 It looks at the features of a new example and calculates how likely those features are under each class. 🔹 Using Bayes' Theorem, it combines this information to estimate the most likely class. 🔹 The "naive" assumption? It assumes each feature contributes independently to the prediction. Why use Naive Bayes? ✅ Extremely fast to train and predict. ✅ Works well with high-dimensional data like text classification. ✅ Performs surprisingly well with relatively small datasets. Keep in mind: ⚠️ The independence assumption is often unrealistic. ⚠️ If features are highly correlated, performance can suffer. #ProbabilisticModels #MachineLearningAlgorithms #NLP #DataScienceEducation #AIExplained

Data Science Foundry

Open In TikTok:

Region: US

Tuesday 21 July 2026 11:30:00 GMT