Скачать или смотреть Part199 Descriptive and predictive modeling in decision tree

In this YouTube Short, we’ll dive into an important concept in Artificial Intelligence (AI) and Machine Learning (ML) — Descriptive and Predictive Modeling using Decision Trees! 🌳

A Decision Tree is one of the most powerful and interpretable algorithms in supervised learning. It not only helps in making predictions but also provides valuable insights about data — making it useful for both descriptive and predictive modeling.

✅ What You’ll Learn in This Short:

Descriptive Modeling in Decision Trees:

Descriptive modeling focuses on understanding and summarizing the patterns in data.

It explains why certain outcomes occur.

The decision tree visually represents how features influence the target variable.

Each split and branch describes relationships between variables, making it easy to interpret patterns.
Predictive Modeling in Decision Trees:

Predictive modeling is about forecasting outcomes for new or unseen data.

The Decision Tree learns from historical data and predicts the target variable based on learned rules.

For classification, it predicts labels like “Yes/No” or “Spam/Not Spam.”

For regression, it predicts continuous values like “price,” “sales,” or “temperature.”