Bias-Variance Tradeoff

Summary

Bias is error from overly simplistic assumptions (underfitting), variance is error from being overly sensitive to training data specifics (overfitting) — total error is a tradeoff between the two.

How to Learn This

1Sketch the classic bias-variance tradeoff curve and label both zones.
2Connect a high-bias and a high-variance model type to real algorithms you'll learn.
3Practice explaining this tradeoff to someone with no ML background.

Overfitting & Underfitting

Cross-Validation

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised vs Reinforcement Learning Train / Test / Validation Split Overfitting & Underfitting Cross-Validation Linear Regression Logistic Regression Decision Trees Random Forests Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Support Vector Machines Naive Bayes

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