The trade-off between machine learning performance and privacy

Complex machine learning models can learn intricate patterns but risk overfitting, leading to poor performance on new data similar to training data.

Parameters in machine learning models are like knobs that can be adjusted to optimize performance, derived from training data.

Research shows machine learning systems memorize data to learn patterns, affecting privacy and raising concerns about overfitting and predictive accuracy.

Read at Fast Company