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Data Preparation
Train test split Feature matrix and target Standard scaling Min-max scaling One-hot encoding Ordinal encoding Missing value imputation Pipeline preprocessing
Regression
Linear regression Ridge regression Lasso regression Polynomial features Mean squared error Mean absolute error R squared Residual inspection
Classification
Logistic regression Decision tree classifier Random forest classifier K nearest neighbors Support vector classifier Naive Bayes Probability prediction Decision threshold
Evaluation
Accuracy Precision Recall F1 score Confusion matrix ROC AUC Cross validation Stratified split
Unsupervised Learning
K-means clustering Cluster inertia Silhouette score DBSCAN Principal component analysis Explained variance Nearest neighbors search Anomaly detection
Feature Engineering
Interaction feature Log transform Binning Polynomial feature expansion Feature selection Mutual information Permutation importance Class weighting
Model Selection
Grid search Randomized search Validation curve Learning curve Baseline model Bias variance tradeoff Overfitting check Underfitting check
Production
Serialize model Load model Batch prediction Single prediction API shape Drift metric Input schema check Reproducible random state Model version metadata
Machine Learning / Data Preparation

Min-max scaling

Learn Min-max scaling in Machine Learning with a dedicated explanation, a topic-specific example, edge guidance, and a mastery check.

12 min read • Beginner
machine-learning-004.py
python 
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler().fit(X_train); X_train_scaled=scaler.transform(X_train)
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