What is Feature Scaling?

import pandas as pd

# Patient features — raw scales
patient = {
    "age":            45,        # years: 0–120
    "weight_kg":      82,        # kg: 30–300
    "serum_creatinine": 1.2,     # mg/dL: 0.5–15
    "num_medications": 8,        # count: 0–50
    "systolic_bp":    138,       # mmHg: 70–250
}

# Problem 1: Distance is dominated by large-magnitude features
# Euclidean distance between two patients:
# Δage=5, Δweight=30, Δcreatinine=0.1, Δmeds=2, Δbp=20
# Raw distance ≈ sqrt(5² + 30² + 0.1² + 2² + 20²) ≈ 36.4
# Creatinine contributes almost nothing — even though it's clinically important

Algorithm          Why raw scale is a problem
─────────────────────────────────────────────────────────────────
k-NN               Distance is dominated by large features
k-Means            Same — cluster centers pulled toward large scales
SVM                Decision boundary depends on distances in feature space
Neural Networks    Large inputs → large gradients → unstable learning
Linear/Logistic    Gradient descent converges slowly; weights aren't comparable
PCA                Variance is dominated by large-scale features
Ridge/Lasso        Penalty is applied to weights, which vary by feature scale

Algorithm          Why raw scale is OK
─────────────────────────────────────────────────────────────────
Decision Tree      Splits on thresholds — scale-invariant
Random Forest      Same — trees use threshold comparisons
Gradient Boosting  Same — tree-based, threshold splits only
Naive Bayes        Works on distributions per feature independently

from sklearn.preprocessing import MinMaxScaler, StandardScaler
import numpy as np

# Before scaling
X_raw = np.array([
    [45,  82, 1.2,  8, 138],
    [67, 110, 4.5, 15, 165],
    [32,  58, 0.8,  3, 115],
])

print("Raw feature ranges:")
print(f"  age:         {X_raw[:, 0].min():.1f} – {X_raw[:, 0].max():.1f}")
print(f"  weight:      {X_raw[:, 1].min():.1f} – {X_raw[:, 1].max():.1f}")
print(f"  creatinine:  {X_raw[:, 2].min():.1f} – {X_raw[:, 2].max():.1f}")

# After MinMax scaling
scaler_mm = MinMaxScaler()
X_mm = scaler_mm.fit_transform(X_raw)

print("\nMinMax scaled ranges:")
print(f"  All features: {X_mm.min():.1f} – {X_mm.max():.1f}")   # 0.0 – 1.0

# After Standard scaling
scaler_std = StandardScaler()
X_std = scaler_std.fit_transform(X_raw)

print("\nStandard scaled stats:")
print(f"  Means: {X_std.mean(axis=0).round(2)}")   # ≈ [0, 0, 0, 0, 0]
print(f"  Stds:  {X_std.std(axis=0).round(2)}")    # ≈ [1, 1, 1, 1, 1]

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# WRONG — scaler sees test data (data leakage)
scaler = StandardScaler()
X_all_scaled = scaler.fit_transform(X)   # computed on all data
X_train_scaled = X_all_scaled[:len(X_train)]
X_test_scaled  = X_all_scaled[len(X_train):]

# CORRECT — scaler fitted on training data only
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)   # fit + transform train
X_test_scaled  = scaler.transform(X_test)         # transform only (use train stats)

# Why: in production, you won't have access to future data.
# The scaler's mean and std must come from training data alone.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

# Pipeline: scaling → model
pipeline = Pipeline([
    ("scaler", StandardScaler()),
    ("model",  LogisticRegression(max_iter=1000)),
])

# cross_val_score fits the scaler on each training fold, applies to val fold
# No leakage — the pipeline handles it correctly
scores = cross_val_score(pipeline, X, y, cv=5, scoring="roc_auc")
print(f"Pipeline CV AUC: {scores.mean():.3f} ± {scores.std():.3f}")

from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import numpy as np

# Demonstrate the impact: k-NN with and without scaling
# (features: age 0-80, serum_creatinine 0.5-15 — very different scales)

knn = KNeighborsClassifier(n_neighbors=5)

# Without scaling
scores_raw = cross_val_score(knn, X_raw, y, cv=5, scoring="roc_auc")
print(f"k-NN unscaled:  {scores_raw.mean():.3f}")

# With scaling
pipeline_knn = Pipeline([("scaler", StandardScaler()), ("knn", knn)])
scores_scaled = cross_val_score(pipeline_knn, X_raw, y, cv=5, scoring="roc_auc")
print(f"k-NN scaled:    {scores_scaled.mean():.3f}")
# Typically a large improvement — sometimes 10-20% AUC difference