Related to Overfitting
Ways to Reduce Bias and Variance:
What is Bias in Machine Learning?
Bias occurs when a model produces consistently unfair or inaccurate results, usually caused during training due to design choices.
What Does High Bias Mean for a Machine Learning Model?
High bias refers to a situation where a model has a strong and often simplistic assumption about the underlying data, leading to underfitting.
It is biased to the data.
What is the Variance of a Machine Learning Model?
Variance measures how much a model's predictions change when trained on different subsets of the training data. It indicates how much the model overfits the training data.
What is the Difference Between Bias and Variance in Machine Learning?
- Bias: The error that occurs when the model cannot learn the true relationship between input and output variables.
- Variance: The error that arises when the model is too sensitive to the training data and does not generalize well to new data.
Explain the Bias-Variance Trade-off in the Context of Model Complexity:
The bias-variance trade-off describes the relationship between model complexity and performance.
- High bias (underfitting) occurs when a model is too simple, leading to poor performance on both training and test data.
- High variance (overfitting) happens when a model is overly complex, performing well on training data but poorly on unseen data.