23 lines
927 B
Markdown
23 lines
927 B
Markdown
# Classification Basics
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## What is Classification?
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**Classification** is the process of predicting the **class label** of a data item.
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- **Goal**: To assign a category to a new item based on past data.
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- **Example**:
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- Input: A bank loan application.
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- Output Class: "Safe" or "Risky".
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## Classification vs Prediction
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- **Classification**: Predicts a **category** (Discrete value).
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- *Example*: Yes/No, Red/Blue/Green.
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- **Prediction (Regression)**: Predicts a **number** (Continuous value).
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- *Example*: Predicting the price of a house ($500k, $505k...).
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## The Process
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1. **Training Phase (Learning)**:
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- The algorithm learns from a "Training Set" where the correct answers (labels) are known.
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- It builds a **Model** (e.g., a Decision Tree).
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2. **Testing Phase (Classification)**:
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- The model is tested on new, unseen data ("Test Set").
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- We check the **Accuracy**: Percentage of correct predictions.
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