In the Bias-Variance Tradeoff, what component of error is reduced by increasing model complexity?

Show answer

Bias

Bias is error due to erroneous assumptions in the learning algorithm, often resulting in underfitting. Increasing model complexity allows the model to represent more complex relationships, reducing bias but typically increasing variance.

Would you remember this in 6 months?

Trivia Forge uses spaced repetition so facts stick. Free, no signup needed.

Turn on Machine Learning practice →

More Machine Learning trivia

In the bias-variance tradeoff, what usually happens to the variance as the model complexity (degrees of freedom) increases?
In the context of bias-variance tradeoff, does a high-bias model typically underfit or overfit the data?
In the context of dimensionality reduction, PCA identifies directions that maximize what statistical property?
In the context of ensemble learning, what is the term for sampling the training data with replacement to create multiple diverse models?
In the context of LSTMs, which gate decides what information from the previous cell state should be discarded?
In the context of Recurrent Neural Networks (RNNs), what phenomenon occurs when gradients converge to zero, making it difficult to learn long-range dependencies?

See all 98 →