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---
id: 2023-12-18
aliases: December 18, 2023
tags:
- link-note
- Data-Science
- Machine-Learning
- Bias-and-Variance
---
# Bias
## Training Data (80~90%) vs. Test Data (10~20%)
## Complexity
- Complexity increases from linear to non-linear models
- Under-fitting: occurs when there is a lot of data
- Over-fitting: occurs when there is little data
## Bias and Variance
- Bias and variance are both types of error in an algorithm.
- $\begin{align} MSE (\hat{\theta}) \equiv E_{\theta} ((\hat{\theta} - \theta)^2) & = E((\hat{\theta} - E(\hat{\theta}) + E(\hat{\theta} - \theta)^2) \\ & = E((\hat{\theta} - E(\hat{\theta}))^2 + 2((\hat{\theta} - E(\hat{\theta}))(E(\hat{\theta}) - \theta)) + (E(\hat{\theta}) - \theta)^2) \\ & = E((\hat{\theta} - E(\hat{\theta}))^2) + 2(E(\hat{\theta}) - \theta)E(\hat{\theta} - E(\hat{\theta})) + (E(\hat{\theta}) - \theta)^2 \\ & = E((\hat{\theta} - E(\hat{\theta}))^2) + (E(\hat{\theta}) - \theta)^2 \\ & = Var_{\theta}(\hat{\theta}) + Bias_{\theta}(\hat{\theta}, \theta)^2 \end{align}$
- $\bbox[teal,5px,border:2px solid red] { MSE (\hat{\theta}) = E((\hat{\theta} - E(\hat{\theta}))^2) + (E(\hat{\theta}) - \theta)^2 = Var_{\theta}(\hat{\theta}) + Bias_{\theta}(\hat{\theta}, \theta)^2 }$
- Bias: under-fitting
- Variance: over-fitting
![[Pasted image 20231218005054.png]]
![[Pasted image 20231218005035.png]]
## Trade-off
- Solution
- Use validation data set
- $\bbox[teal,5px,border:2px solid red]{\text{Train data (80\%)+ Valid data (10\%) + Test data (10\%)}}$
- Cannot directly participate in model training
- Continuously evaluates in the learning base, and stores the best existing performance
- K-fold cross validation
- **Leave-One-Out Cross-Validation (LOOCV)**
- a special case of k-fold cross-validation where **K** is equal to the number of data points in the dataset.
- What if **K** becomes bigger?
1. train data $\uparrow$
2. bias error $\downarrow$ and variance error $\uparrow$
3. cost $\uparrow$
- [[Regularization]] loss function
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