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When deciding which predictive model to use, understanding and managing bias and variance are crucial. Here’s a list of key considerations for bias and variance in predictive modeling:
I. Bias
1. Underfitting (High Bias):
- Models with high bias may oversimplify the underlying patterns in the data and perform poorly on both training and test sets.
2. Model Complexity:
- Bias tends to increase as model complexity decreases.
- Choosing a model with sufficient complexity to capture the underlying patterns is essential.
3. Feature Selection:
- Inadequate feature selection may lead to biased models.
- It’s crucial to include relevant features while avoiding overfitting.
4. Algorithm Choice:
- Different algorithms have varying levels of bias.
- Understanding the characteristics of the problem and the data helps choose an algorithm with an appropriate bias-variance trade-off.
