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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

Pipeline preprocessing

Learn Pipeline preprocessing in Machine Learning with a dedicated explanation, a topic-specific example, edge guidance, and a mastery check.

12 min read • Beginner

machine-learning-008.py

python

from sklearn.pipeline import Pipeline
pipe=Pipeline([("scale",StandardScaler()),("model",LogisticRegression(max_iter=1000))]); pipe.fit(X_train,y_train)

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

Pipeline preprocessing Mastery Check →

Missing value imputation

Linear regression