100 Facts About Trade-offs in Machine Learning

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Here’s a list of 100 facts about trade-offs in machine learning:

1. Bias-Variance Trade-off: There is a trade-off between bias and variance in model performance. A model with high bias tends to oversimplify the data, while high variance can lead to overfitting.
2. Accuracy-Interpretability Trade-off: More complex models may achieve higher accuracy but are often less interpretable, making it challenging to understand the model’s decision-making process.
3. Computational Complexity-Model Performance Trade-off: More complex models often require more computational resources, and there is a trade-off between model complexity and computational efficiency.
4. Underfitting-Overfitting Trade-off: Balancing underfitting (model too simple) and overfitting (model too complex) is a fundamental trade-off in machine learning.
5. Feature Selection-Model Complexity Trade-off: Including more features in a model can increase its complexity, but too many irrelevant features may lead to overfitting. Feature selection aims to find the right balance.
6. Training Time-Model Performance Trade-off: Some powerful models require longer training times. Choosing a model involves considering the trade-off between training time and desired performance.