Learn Before
General Implications of MSE for Machine Learning
As the equation for the Mean Squared Error Suggests, both natural variance of the data itself as well as biases reflected in subsets of data can be the sources of error for a given machine learning system.
The book provides this chart as a general rule for how training a machine learning system removes bias, but also may increase variance.
This implies that for a given machine learning structure there is an optimal capacity for how much the system can correctly recognize.
The exact optimal capacity will likely depend on the size and design of the system as well as the innate complexity of the problem itself.
0
1
Tags
Data Science
Related
Derivation for MSE to Bias Squared Plus Variance
General Implications of MSE for Machine Learning
Two different statistical models, Model A and Model B, are used to estimate a true parameter value which is known to be 100. After generating a large number of predictions with both models, the following observations are made:
- The average of all predictions from Model A is 105. The individual predictions from Model A are all very close to each other.
- The average of all predictions from Model B is 100. The individual predictions from Model B are spread out over a wide range of values.
Given that the total expected squared error of an estimator can be decomposed into two primary components, which statement best analyzes the error characteristics of these two models?
Calculating Error Components of a Statistical Estimator
A machine learning engineer is comparing two estimators, Estimator A and Estimator B, to predict a certain value. The primary goal is to minimize the expected squared error. After analysis, the following characteristics are determined:
- Estimator A: Has a bias of 0 and a variance of 4.
- Estimator B: Has a bias of 1 and a variance of 2.
Which estimator should be chosen, and why?
When comparing two statistical estimators for a specific task, the estimator with the lower bias will always result in a lower overall Mean Squared Error.