====== 🔁 Five-Fold Cross-Validation ======

**Five-fold cross-validation (5-fold CV)** is a statistical method used to **evaluate the performance of a machine learning model**. It helps ensure the model generalizes well to unseen data.

===== 🧠 What Is It? =====

* The dataset is randomly split into **5 equal parts (folds)**.
* The model is trained on **4 folds** and tested on the **remaining 1 fold**.
* This process repeats **5 times**, each time using a different fold as the test set.
* Final performance is the **average** of the 5 results.

===== 📊 Why Use It? =====

* Reduces **overfitting risk**
* Provides a **more reliable estimate** of model performance than a single train/test split
* Especially useful when data is **limited**

===== 🔢 Example Workflow =====

  - Total samples = 100 patients with MRI radiomics
  - Split into 5 folds of 20 patients each:
    * Iteration 1: Train on folds 1–4, test on fold 5
    * Iteration 2: Train on folds 1,2,3,5, test on fold 4
    * …
    * Iteration 5: Train on folds 2–5, test on fold 1
  - Compute metrics (e.g., accuracy, AUC) at each step
  - Final result: **mean of all 5 metrics**

===== ✅ Advantages =====

  * Uses **all data** for both training and testing
  * Provides **variance estimate** of model performance
  * Good balance between **bias and variance**

===== ⚠️ Notes =====

  * Use **stratified** 5-fold CV when class labels are imbalanced
  * Avoid data leakage: normalize or extract features **within each fold**

===== 🛠 Python Snippet =====

<code python>
from sklearn.model_selection import cross_val_score
from xgboost import XGBClassifier

model = XGBClassifier()
scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')

print("5-fold CV Accuracy:", scores.mean())
</code>