Which of the following lists examples of high-bias but low-variance machine learning algorithms?

[izxi]

Which of the following lists examples of high-bias but low-variance machine learning algorithms?

Select one:

• A. Linear Regression, Decision Trees, Logistic Regression
• B. Decision Trees, Linear Discriminant Analysis, Support Vector Machines
• C. Linear Regression, Linear Discriminant Analysis, Logistic Regression
• D. Linear Regression, k-Nearest Neighbours, Logistic Regression

[izxi]

High bias is equivalent to aiming in the wrong place. High variance is equivalent to having an unsteady aim.

This can lead to the following scenarios:

• Low bias, low variance: Aiming at the target and hitting it with good precision.
• Low bias, high variance: Aiming at the target, but not hitting it consistently.
• High bias, low variance: Aiming off the target, but being consistent.
• High bias, high variance: Aiming off the target and being inconsistent.

[alokkumar70]

Linear Regression, Linear Discriminant Analysis, Logistic Regression

[johnnieng]

C. Linear Regression, Linear Discriminant Analysis, Logistic Regression *

[iaminebriki]

The correct answer is option: C. Linear Regression, Linear Discriminant Analysis, and Logistic Regression.

In general, linear machine learning algorithms like Linear Regression, Linear Discriminant Analysis and Logistic Regression will have high-bias and low-variance. The non- linear machine learning algorithms like Decision Trees, k-Nearest Neighbors and Support Vector Machines will have low-bias and high-variance.