MLS-C01 Question 100
Select 3You are working on a machine learning project that involves building a predictive model using a dataset of customer reviews. The dataset consists of text data, audio recordings of customer feedback, and associated metadata such as timestamps. You need to preprocess the data and extract relevant features for your model. Which of the following feature extraction techniques should you use?
- A
Use TF-IDF to extract features from the text data.
- B
Apply Mel-frequency cepstral coefficients (MFCCs) to extract features from the audio recordings.
- C
Use Principal Component Analysis (PCA) directly on the raw text data.
- D
Extract sentiment scores from the text data as features.
- E
Use Amazon Rekognition to extract features from the audio recordings.
Show answer and explanation
Correct answers: A, B, D
Explanation
To effectively preprocess and extract features from datasets involving multiple data types, such as text and audio, appropriate techniques must be applied based on the data modality. TF-IDF is a standard approach for transforming text into numerical features, while MFCCs are commonly used for feature extraction in audio data. Additionally, deriving sentiment scores from text can provide valuable insights for predictive modeling. The other options are either inappropriate for the given data type or misaligned with the problem requirements.
- A. Correct.
TF-IDF (Term Frequency-Inverse Document Frequency) is a common and effective technique for extracting features from text data. It transforms the text into numerical representations that can be used for machine learning models.
- B. Correct.
Mel-frequency cepstral coefficients (MFCCs) are widely used to extract features from audio recordings, as they capture the frequency and timbre information necessary for modeling speech and audio data.
- C. Incorrect.
PCA (Principal Component Analysis) is not suitable for raw text data. Text data must first be converted into numerical representations, such as through TF-IDF or word embeddings, before PCA can be applied.
- D. Correct.
Sentiment scores can be valuable features extracted from text data, especially in the context of customer reviews, as they provide additional insights into the emotion or opinion expressed in the text.
- E. Incorrect.
Amazon Rekognition is a service designed for image and video analysis, not audio recordings. It cannot be used to extract features from audio data.