[부스트캠프 AI Tech 5기] Pre-Course : (21) 최적화의 주요 용어 이해하기

📖 Introduction

• Gradient Descent
  • First-order iterative optimization algorithm for finding a local minimum of a differentiable function.

📖 Generalization

• How well the learned model will behave on unseen data.
  

📖 Underfitting vs. Overfitting

📖 Cross-validation

• Cross-validation is a model validation technique for assessing how the model will generalize to an independent (test) data set.
  

📖 Bias and Variance

📖 Bias and Variance Tradeoff

• We can derive that what we are minimizing (cost) can be decomposed
  into three different parts: bias2, variance, and noise.

📖 Bootstrapping

• Bootstrapping is any test or metric that uses random sampling with replacement.

📖 Bagging vs. Boosting

• Bagging (Bootstrapping aggregating)
  • Multiple models are being trained with bootstrapping.
  • ex) Base classifiers are fitted on random subset where individual predictions are aggregated (voting or averaging).
• Boosting
  • It focuses on those specific training samples that are hard to classify.
  • A strong model is built by combining weak learners in sequence where each learner learns from the mistakes of the previous weak learner.

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