AWS Certified Cloud Practitioner

Explanation

In machine learning, bias refers to the error introduced by approximating a real-world problem with a simplified model. This is often called underfitting, where the model is too simple to capture the underlying patterns in the data.

Let's break down each option:

• Option A (Correct): "Error caused by the model being overly simple" - This accurately describes bias in ML. High bias means the model makes strong assumptions about the data and fails to capture its complexity.

• Option B: "Model sensitivity to fluctuations in training data" - This describes variance, not bias. Variance refers to how much the model's predictions would change if trained on different data.

• Option C: "Random variations in predictions" - This also describes variance or noise, not bias.

• Option D: "The storage cost of model parameters" - This is unrelated to the statistical concept of bias in ML.

Key Concept: Bias-Variance Tradeoff

In machine learning, there's a fundamental tradeoff between bias and variance:

• High bias, low variance: Simple models that are consistent but inaccurate (underfitting)

• Low bias, high variance: Complex models that fit training data well but may not generalize (overfitting)

The goal is to find the right balance that minimizes total error.