What is Model Fit in Machine Learning? | Overfitting, Underfitting & How to Fix Them

Описание: Welcome to this lecture on Model Fit in Machine Learning, where we’ll explore one of the most important concepts in AI — how well a model learns from data and how to balance accuracy and generalization. 🤖

In this session, we’ll break down the difference between overfitting and underfitting, understand how they impact model performance, and discuss practical ways to fix them using real-world examples.

Here’s what you’ll learn:
🔹 What “fit” means in Machine Learning
🔹 What is a good fit and why it matters
🔹 What causes overfitting (too complex models) and underfitting (too simple models)
🔹 How to detect each using training vs test data
🔹 Techniques to reduce overfitting and improve generalization
🔹 Methods to fix underfitting by improving model complexity and training time

By the end of this lecture, you’ll have a solid understanding of how to train models that perform well on new, unseen data — a key skill for every AI and ML engineer.

🧠 Key Topics Covered

✅ Definition of “Fit” in ML
✅ Overfitting vs Underfitting — Differences Explained
✅ Training vs Testing Data Split (80-10-10 Rule)
✅ Real-World Example: Spam Detection
✅ Bias vs Variance Relationship
✅ Techniques to Fix Overfitting
✅ Techniques to Fix Underfitting

⏱️ Timestamps

00:00 – Introduction
00:23 – What is model fit?
00:44 – Training a model
01:01 – What is a good fit?
01:13 – Overfitting explained
01:42 – Underfitting explained
02:01 – Data split for model training (80/10/10)
02:39 – Example: Email spam classification
03:01 – Visualization of overfitting vs underfitting
04:19 – Differences between overfitting & underfitting
05:35 – Fixing overfitting (simplify, regularize, early stop)
07:38 – Fixing underfitting (increase complexity, more epochs)
08:30 – Summary & best practices

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