IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

The bias-variance tradeoff describes the fundamental tension between two sources of error in machine learning models: bias and variance. Bias refers to the error due to overly simplistic assumptions in the learning algorithm, leading to systematic deviations from the real relationships in the data. High bias can result in an underfitting model that does not capture the underlying trends.

Variance, on the other hand, represents the error caused by the model's sensitivity to the fluctuations in the training data. A model with high variance pays too much attention to the noise in the training data, which can lead to overfitting—where the model performs well on training data but poorly on unseen data.

Balancing these two sources of error is crucial for building effective predictive models. Therefore, minimizing the total error involves finding a sweet spot where bias and variance are appropriately balanced, thus achieving better generalization to new data.

Other options do not accurately capture the essence of the bias-variance tradeoff. The interplay between feature selection and model accuracy pertains to how features impact model performance, but it does not describe the bias-variance relationship. The relationship between different classifiers can indicate performance but lacks the specific nuance of error sources. Lastly, the cost of training versus testing datasets relates more to data handling strategy