52. The Rule That Prevents You From Cheating Your Own Model

It's crucial to follow a structured and methodical workflow when working with machine learning models, especially in ensuring that your model is robust and generalizes well to unseen data. Here’s an elaboration on the steps you provided:

Workflow Steps

1. Load Data: Start by loading your dataset into memory.

   python
    
   1from sklearn.datasets import load_breast_cancer
   2data = load_breast_cancer()
   3X, y = data.data, data.target

2. Split the Data First:

   • Splitting the data before any preprocessing ensures that your test set remains unseen and unbiased during model selection.

   python
    
   1from sklearn.model_selection import train_test_split
   2
   3X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

3. Preprocess After Splitting:

   • Preprocessing should be done separately on the training and testing datasets to avoid data leakage.

   python
    
   1from sklearn.preprocessing import StandardScaler
   2
   3scaler = StandardScaler()
   4X_train = scaler.fit_transform(X_train)  # Fit only on

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