Primus Global Technologies Payment Posting Interview Questions and Answers

p value is a statistical measure that helps determine the significance of a hypothesis test.

• p value is the probability of obtaining a result as extreme or more extreme than the observed result, assuming the null hypothesis is true.

• A p value of less than 0.05 is considered statistically significant.

• Spacy is an open-source software library for advanced natural language processing (NLP).

• NLP is a field of study that focuse...read more

Lambda function is an anonymous function in Python that can take any number of arguments and can only have one expression.

• Lambda functions are defined using the keyword 'lambda'.

• They are commonly used with built-in functions like filter(), map(), and reduce().

• Lambda functions can be used to create small, throwaway functions that are not needed elsewhere in the code.

• They are often used to write more concise and readable...read more

Attention focuses on specific parts of input data, while self attention considers relationships within the input data itself.

• Attention is used in models like seq2seq for machine translation to focus on relevant parts of the input sequence.

• Self attention is used in transformer models to capture dependencies between different words in a sentence.

• Attention mechanisms can be either global or local, while self attention is

Handling null values is crucial for data integrity and analysis.

• Identify null values in the dataset using functions like isnull() or isna()

• Decide on the best strategy to handle null values - imputation, deletion, or flagging

• Impute missing values using mean, median, mode, or predictive modeling techniques

• Delete rows or columns with a high percentage of missing values if they cannot be imputed

• Flag null values to distingu

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

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.

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