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Phase 02: ML Fundamentals

AI From Scratch/Lesson 17/~90 minutes

Handling Imbalanced Data

When 99% of your data is "normal," accuracy is a lie.

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Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Resources

SMOTE: Synthetic Minority Over-sampling Technique (Chawla et al., 2002)Reading Learning from Imbalanced Data (He & Garcia, 2009)Reading imbalanced-learn documentationReading The Precision-Recall Plot Is More Informative than the ROC Plot (Saito & Rehmsmeier, 2015)Reading Imbalanced DataSkill imbalanced.pyCode