Modeling Intro

What You'll Learn

• What is a model?
• Supervised vs Unsupervised learning
• Regression vs Classification
• Train/Test split concept
• Overfitting vs Underfitting

What is a Model?

A model is a simplified representation of reality. In data science, it's a mathematical function that maps inputs (features) to outputs (predictions).

$$ y = f(x) + epsilon $$

Where:

• $y$ is the target (what we want to predict)
• $x$ is the features (data we have)
• $epsilon$ is the error (noise)

Types of Learning

1. Supervised Learning: We have labeled data (we know the answer).

• Regression: Predicting a number (e.g., Price, Temperature).
• Classification: Predicting a category (e.g., Spam/Not Spam, Cat/Dog).

2. Unsupervised Learning: We don't have labels. We look for patterns.

• Clustering: Grouping similar items (e.g., Customer Segmentation).
• Dimensionality Reduction: Simplifying data.

Key Concepts

Train/Test Split: Never test your model on the same data you used to teach it!

1. Training Set (70-80%): Used to learn the patterns.
2. Test Set (20-30%): Used to evaluate performance on unseen data.

Overfitting vs Underfitting:

• Underfitting: Model is too simple. It doesn't learn the pattern. (High bias)
• Overfitting: Model is too complex. It memorizes the training data but fails on new data. (High variance)
• Good Fit: Balances bias and variance.

The Modeling Workflow

1. Problem Definition: What are we predicting?
2. Data Collection & Cleaning: Garbage in, garbage out.
3. EDA: Understand the data.
4. Feature Engineering: Create better inputs.
5. Model Selection: Choose an algorithm.
6. Training: Fit the model.
7. Evaluation: Check performance.
8. Deployment: Use it!

Next Steps

Let's build our first simple model using Scikit-Learn!