Supervised vs Unsupervised Learning

These are the two main flavors of ML. Let's break them down simply.

Supervised Learning = Learning with Answers

Think of a teacher showing flashcards: - Shows picture of cat → "This is a cat" - Shows picture of dog → "This is a dog"

After enough examples, the student can identify new animals.

**The key:** You provide labeled data (input + correct answer).

### Supervised Learning Examples:

| Task | Input | Label | |------|-------|-------| | Spam detection | Email text | Spam/Not spam | | House prices | Size, location | Price | | Disease diagnosis | Symptoms | Disease type | | Image recognition | Photo | Object name |

### Two Types of Supervised Learning:

**Classification** - Predicting categories ```python # Is this email spam? prediction = "spam" or "not_spam" ```

**Regression** - Predicting numbers ```python # What's this house worth? prediction = 450000 # dollars ```

Unsupervised Learning = Finding Hidden Patterns

No labels. No right answers. The algorithm explores data on its own.

Like giving someone a box of Legos without instructions—they'll naturally group similar pieces together.

### Unsupervised Learning Examples:

| Task | What it does | |------|--------------| | Customer segmentation | Groups similar customers | | Anomaly detection | Finds unusual transactions | | Topic modeling | Discovers themes in documents | | Recommendation | Finds similar items |

### Main Types:

**Clustering** - Grouping similar items ```python # Which customers are similar? groups = [[user1, user4], [user2, user5], [user3]] ```

**Dimensionality Reduction** - Simplifying data ```python # Reduce 100 features to 10 important ones simplified_data = reduce(complex_data) ```

Quick Comparison

| Aspect | Supervised | Unsupervised | |--------|------------|--------------| | Has labels? | Yes | No | | Goal | Predict specific output | Find patterns | | Evaluation | Easy (compare to answers) | Harder (subjective) | | Data prep | More work (need labels) | Less work | | Examples | Spam filter, price prediction | Customer groups, anomaly detection |

Which Should You Use?

**Use Supervised when:** - You know what you want to predict - You have labeled examples - You need measurable accuracy

**Use Unsupervised when:** - You want to explore data - Labeling is expensive/impossible - You're looking for unknown patterns

Real World Scenario

**E-commerce website:**

Supervised task: "Will this user buy?" (Yes/No labels from history)

Unsupervised task: "What types of shoppers do we have?" (No labels, find natural groups)

Both work together! Use unsupervised to find customer segments, then supervised to predict behavior within each segment.

Key Takeaway

- **Supervised** = "Here's the answer, learn to predict it" - **Unsupervised** = "Find interesting patterns yourself"

Most real projects use both. Start with supervised if you have clear goals and labeled data.