Position: Data Scientist
How was the interview process? What was it like?
What is Precision and Recall?
What technical questions were asked?
What was one of your solutions?
The bias of an estimator is the difference between the expected value and true value. A model with a high bias tends to be oversimplified and results in underfitting. Variance represents the model’s sensitivity to the data and the noise. A model with high variance results in overfitting.
Therefore, the bias-variance tradeoff is a property of machine learning models in which lower variance results in higher bias and vice versa. Generally, an optimal balance of the two can be found in which error is minimized.