Genpact Senior Data Scientist Interview Questions and Answers

Use techniques like regularization, feature selection, cross-validation, and data augmentation.

• Utilize regularization techniques like Lasso or Ridge regression to prevent overfitting.

• Perform feature selection to focus on the most important variables and reduce noise.

• Use cross-validation to assess model performance and generalizability.

• Consider data augmentation techniques like synthetic data generation or bootstrapping...read more

I will lead by setting clear goals, providing guidance, fostering collaboration, and recognizing team achievements.

• Set clear goals and expectations for the team

• Provide guidance and support to team members

• Foster collaboration and communication within the team

• Recognize and reward team achievements

• Lead by example and demonstrate strong work ethic

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...read more

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

BERT is a bidirectional transformer model for pre-training language representations, while GPT is a generative model.

• BERT is a pre-training model that learns contextual representations of words by considering both left and right context.

• GPT is a generative model that uses a transformer decoder to generate text based on the context.

• BERT is bidirectional, meaning it can understand the context of a word by looking at both...read more