The correct answer is C. The balance between a model’s ability to fit training data and its ability to generalize to unseen data.
In machine learning, the bias-variance trade-off is a fundamental concept that affects the performance of any machine learning model. It refers to the trade-off between the model’s ability to fit the training data (bias) and its ability to generalize to unseen data (variance).
A model with high bias will tend to overfit the training data, meaning that it will perform well on the training data but poorly on unseen data. A model with high variance will tend to underfit the training data, meaning that it will perform poorly on both the training data and unseen data.
The goal of machine learning is to find a model that minimizes both bias and variance. This is a difficult task, and there is no single model that will always perform the best. The best model for a given task will depend on the specific data and the desired performance metrics.
Here is a more detailed explanation of each option:
- Option A: The trade-off between model complexity and model performance. This is not the same as the bias-variance trade-off. Model complexity refers to the number of parameters in a model. A more complex model will generally have a better fit to the training data, but it may also be more prone to overfitting. The goal is to find a model that is complex enough to capture the underlying patterns in the data, but not so complex that it overfits the training data.
- Option B: The trade-off between false positives and false negatives. This is a different trade-off that is relevant to classification problems. A false positive is an instance that is classified as positive when it is actually negative. A false negative is an instance that is classified as negative when it is actually positive. The goal of classification is to minimize both false positives and false negatives.
- Option D: The trade-off between precision and recall. This is another trade-off that is relevant to classification problems. Precision is the fraction of positive instances that are correctly classified. Recall is the fraction of all positive instances that are correctly classified. The goal of classification is to maximize both precision and recall.
I hope this explanation is helpful. Please let me know if you have any other questions.