Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written.

You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets.

How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

Question

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written.

You have a large training dataset that is structured like this:

AuthorA:Political Party A

Textal:
Texta2:

AuthorB:Political Party B

TextBL
TextB2:

AuthorC:Political Party B

AuthorD:Political Party A

TextDl:

[Sentenceall,
[Sentencea21,

[SentenceB11,
[SentenceB21,

[Sentencecl1,
[Sentencec21,

[SentenceD11,
[Sentencep21,

SentenceAl2,
SentenceA22,

SentenceBl2,
SentenceB22,

Sentencecl2,
Sentencec22,

SentenceD12,
SentenceD22,

Sentenceal3,
Sentencea23,

SentenceBl3,
SentenceB23,

SentenceCl3,
Sentencec23,

SentenceD13,
SentenceD23,

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets.

How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

Exam-Answer · Accepted Answer

Distribute sentences randomly across the train-test-eval subsets: Train set: [SentenceA11, SentenceA21, SentenceB11, SentenceB21, SentenceC11, SentenceD21 ...] Test set: [SentenceA12, SentenceA22, SentenceB12, SentenceC22, SentenceC12, SentenceD22 ...] Eval set: [SentenceA13, SentenceA23, SentenceB13, SentenceC23, SentenceC13, SentenceD31 ...]

Question 49 of 56 from exam PMLE: Professional Machine Learning Engineer

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