Recall the Brownies

By Vineeta Kommineni posted 24 days ago

  


Eric Weber made an impassioned post on LinkedIn yesterday: "Something that drives me crazy about statistics: Giving important concepts numbers or uninformative names. Example: Naming type 1 and 2 error. Half the time I forget which one is which and have to look it up."

I use mnemonics to help remember easy-to-forget statistical concepts. Here are a few. I hope you find them handy.   

Type 1 & Type 2 Errors

Type 2 Error - failing to reject the null hypothesis: (Fail 2 reject, Type 2)
False Negative (False and negative - 2 negatives, Type 2) 
It follows then: 
Type 1 Error - rejecting the null hypothesis when it is True. False Positive. 


Precision & Recall


Precision
- think of Cookies ('c' in Precision and 'c' in Cookies)
Cookie-cutters help give cookies perfect shape. There is Precision; however, some dough is typically leftover. 
Recall - think of Brownies. There is no dough leftover when you make brownies, but the shape is not perfect. 

Bias & Variance

Bias
- related to assumptions made by a model 
Variance - related to Errors when you change the training data ('r' in Variance, 'r' in Errors) 

Specificity & Sensitivity

Specificity
- ability of a test to correctly identify patients with a disease, say Cancer
('c' in Specificity, 'c' in Correctly, 'c' in Cancer, Correctly Diagnosed with Cancer) 
It follows then: 
Sensitivity - ability of a test to correctly identify people without the disease 

Overfitting & Underfitting

Overfitting
- like overthinking. When you overthink, you see patterns that are not there.
Vineeta is an overthinker. V is also for Variance. 
High Variance, Low Bias. 
It follows then:
Underfitting- underwhelming analysis does not capture trends
Low Variance, High Bias. 

Ridge & Lasso Regression

Ridge Regression
- (2 R's) L2 regularization
It follows: 
Lasso regression - L1 regularization 

Please share your favorite mnemonics in the comments :)

Image Credit: Stocksy

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