What is the difference between squared error and absolute error?

As we see from the definitions of MAE and MSE, the key difference between them is that MAE uses the absolute error whilst MSE uses the squared error. But what is the difference between these two calculations?

The key difference between squared error and absolute error is that squared error punishes large errors to a greater extent than absolute error, as the errors are squared instead of just calculating the difference. 

--https://stephenallwright.com/mse-vs-mae/#:~:text=As%20we%20see%20from%20the,MSE%20uses%20the%20squared%20error.

Tenses in abstract writing

 

• Use present tense while stating general facts
• Use past tense when writing about prior research
• Use past tense when stating results or observations
• Use present tense when stating the conclusion or interpretations
• Use present tense when referring to your study/paper

ML drift

Concept drift vs data drift

https://www.dataversity.net/data-drift-vs-concept-drift-what-is-the-difference/#:~:text=Data%20drift%20refers%20to%20the,of%20a%20machine%20learning%20model.

Data drift คือมีบางช่วงของข้อมูลตอน train ที่ model พยากรณ์ไม่ค่อยแม่น และบังเอิญ test set ก็มีวิวัฒนาการไปในทางที่มีข้อมูลช่วงเหล่านั้นมากขึ้นๆ ทำให้ความแม่นในการพยากรณ์ลดลง 

Concept drift คือ target function หรือ relationship ระหว่าง input feature & output label ค่อยๆวิวัฒนาการเปลี่ยนไป

Label drift and feature drift

https://www.youtube.com/watch?v=uOG685WFO00

Nonlinear correlation

• Pearson’s Correlation Coefficient (R) supports only linear data. R is close to zero for nonlinear correlation, so one had better use distance correlation.
• 

• Distance Correlation (devised in 2005) supports both linear and nonlinear data. (https://en.wikipedia.org/wiki/Distance_correlation)

• Maximal Information Coefficient (devised in 2011) supports both linear and nonlinear data. 

https://www.freecodecamp.org/news/how-machines-make-predictions-finding-correlations-in-complex-data-dfd9f0d87889/

Superfluous citation

 https://www.nature.com/articles/d41586-023-01532-w

Tensor

A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array.

A vector is a one-dimensional or first order tensor and a matrix is a two-dimensional or second order tensor. (i.e. N-order tensor is comparable to N-dimensional array)

Tensor notation is much like matrix notation with a capital letter representing a tensor and lowercase letters with subscript integers representing scalar values within the tensor. Below is third order tensor (i.e. 3 dimensional array)

     t111, t121, t131     t112, t122, t132     t113, t123, t133

T = (t211, t221, t231),  (t212, t222, t232),  (t213, t223, t233)

     t311, t321, t331     t312, t322, t332     t313, t323, t333

cf. https://machinelearningmastery.com/introduction-to-tensors-for-machine-learning/