Coducer Technologies Interview Questions and Answers

Text embeddings are numerical representations of text data that capture semantic meaning.

• Text embeddings convert words or sentences into numerical vectors.

• They are used in natural language processing tasks like sentiment analysis, text classification, and machine translation.

• Popular techniques for generating text embeddings include Word2Vec, GloVe, and BERT.

Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space.

• It measures the cosine of the angle between two vectors.

• Values range from -1 (completely opposite) to 1 (identical), with 0 indicating orthogonality.

• Commonly used in text mining for document similarity and recommendation systems.

Embeddings are generated by converting words or entities into numerical vectors in a high-dimensional space.

• Use pre-trained word embeddings like Word2Vec, GloVe, or FastText

• Train your own embeddings using algorithms like Word2Vec, GloVe, or FastText on a large corpus of text data

• Fine-tune pre-trained embeddings on domain-specific data to improve performance

Handling imbalanced training data is crucial for model performance and accuracy.

• Use techniques like oversampling, undersampling, or SMOTE to balance the dataset

• Utilize algorithms that are robust to imbalanced data, such as Random Forest or XGBoost

• Consider using ensemble methods or cost-sensitive learning to address class imbalance

F Score is a measure of a test's accuracy that considers both the precision and recall of the test.

• F Score is calculated using the formula: 2 * (precision * recall) / (precision + recall)

• It is used in binary classification tasks to balance precision and recall.

• A high F Score indicates a model with both high precision and high recall.

TFIDF stands for Term Frequency-Inverse Document Frequency, a numerical statistic that reflects how important a word is to a document in a collection or corpus.

• TFIDF is used in natural language processing to evaluate the importance of a word in a document relative to a collection of documents.

• It combines two metrics: term frequency (TF) and inverse document frequency (IDF).

• TFIDF helps in identifying the significance of a word in a document by considering how frequently it app...read more