Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › Artificial Intelligence › Machine Learning & Data

Classification

concept · part of Machine Learning & Data

A machine learning task that predicts a categorical target variable, here NEIGHBORHOOD.

Inside Classification (5)

• Confusion matrix — Table showing true positives, true negatives, false positives, and false negatives; basis for other classification metrics.
• F1 score — Harmonic mean of precision and recall; balanced metric for imbalanced datasets.
• Precision — Proportion of positive predictions that are correct; important when false positives are costly.
• Recall — Proportion of actual positives correctly identified; important when missing positives is costly.
• ROC curve — Plot of true positive rate against false positive rate across thresholds; AUC measures class separation.

Connections

• Uses XGBoost
• Uses Snowpark ML
• Related to XGBClassifier
• Related to XGBoost
• Related to Snowflake Cortex ML functions
• Builds on LDA
• Prerequisite of Deep Learning

This is the text view of an interactive 3D knowledge graph — open this page with JavaScript enabled to explore it visually.