MLS-C01 · Question #86
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and…
The correct answer is D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles. word2vec provides content of the sentences which is very important for evaluation of the risk.
Question
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters. Which approach will provide the MAXIMUM performance boost?
Options
- AInitialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on
- BUse gated recurrent units (GRUs) instead of LSTM and run the training process until the
- CReduce the learning rate and run the training process until the training loss stops decreasing.
- DInitialize the words by word2vec embeddings pretrained on a large collection of news articles
How the community answered
(27 responses)- A4% (1)
- B15% (4)
- C22% (6)
- D59% (16)
Explanation
word2vec provides content of the sentences which is very important for evaluation of the risk.
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