AI & Machine Learning · Quick Reference

1.2 Preprocessing: Scaling, Normalization & Encoding Cheat Sheet

AI & Machine Learning

In one line: Imagine you're predicting house prices. Two features:

Key Ideas

1Why Preprocessing Matters. Imagine you're predicting house prices. Two features:

2StandardScaler — Zero Mean, Unit Variance. The most common scaler. For each feature, it subtracts the mean and divides by the standard deviation:

3MinMaxScaler — Compress to a Fixed Range. Scales each feature to a specific range, usually [0, 1]:

4RobustScaler — Handles Outliers. Uses the median and the interquartile range (IQR) instead of mean and std. Outliers have much less influence:

5Which Scaler to Choose Decision guide Does your data have significant outliers? ├── Yes → RobustScaler └── No → Does the model need values in [0,1]? ├── Yes → MinMaxScaler └── No → StandardScaler (default) ScalerCenters onScale unitOutlier resistantBest for StandardScalerMeanStd deviationNoMost models (default) MinMaxScalerMin valueValue rangeNoNeural nets, image data RobustScalerMedianIQRYesData with real outliers None———Tree-based models (RF, XGBoost) Encoding Categorical Variables. Models work with numbers. A column containing "red", "green", "blue" needs to become numbers. There are two main approaches, and choosing wrong causes subtle bugs.

Code Examples

from sklearn.preprocessing import StandardScaler
import numpy as np

X_train = np.array([[25], [50], [75], [150]])
X_test  = np.array([[40], [100]])

scaler = StandardScaler()
scaler.fit(X_train)              # learn μ and σ from train

X_train_sc ...

from sklearn.preprocessing import OrdinalEncoder
import numpy as np

# ✅ CORRECT: size has a real order
X = np.array([['small'], ['large'], ['medium'], ['small']])

enc = OrdinalEncoder(categories=[['small', 'medium', 'large']])
enc.fit(X)
print(enc...

from sklearn.preprocessing import OneHotEncoder
import numpy as np

X = np.array([['red'], ['green'], ['blue'], ['red']])

enc = OneHotEncoder(sparse_output=False)
enc.fit(X)

X_encoded = enc.transform(X)
print(enc.categories_)     # [['blue', 'gre...