Machine Learning Part 2: Prediction and Classification

Summary

• Original article title, translated: "Machine Learning Part 2: Prediction and Classification." Only the filename's unsupported colon was replaced. Published 2025-02-10 · slug machine-learning-second-journey · category "AI and machine learning studies / ML foundations and practice" · source article recorded in Korean and English.
• The second learning record: after covering library fundamentals in the first article, I built prediction and classification models myself.
• Code examples cover simple and multiple linear regression, binary and multiclass logistic regression, K-Fold cross-validation, and hyperparameter tuning with GridSearchCV.
• The practical section is a house-price prediction mini-project using preprocessing, scaling, and Random Forest, with feature importance used to interpret the results.
• Reflection: logistic regression can be unstable without scaling, and interpretability is useful in practice. The next step is ensembles, followed by deep learning.

Mentioned

• No organization or project links; this is a learning record.

Connections

• Writing · Learning the Python ecosystem for machine learning
• Concepts: i18n and multilingual support · Cross-validation · Running a technical blog · Logistic regression · Explainable anomaly detection · Ensembles · Hyperparameter tuning