New York Life Insurance Company Data Scientist Interview Questions and Answers

Supervised learning is a type of machine learning where the model is trained on labeled data to make predictions or decisions.

• Uses labeled data for training

• Predicts outcomes based on input features

• Examples include regression and classification algorithms

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data without any predefined output labels.

• No predefined output labels are provided for the training data

• The model must find patterns and relationships in the data on its own

• Common techniques include clustering and dimensionality reduction

• Examples: K-means clustering, Principal Component Analysis (PCA)

LSTM RNN is a type of RNN that can learn long-term dependencies, while simple RNN struggles with vanishing/exploding gradients.

• LSTM RNN has more complex architecture with memory cells, input, forget, and output gates.

• Simple RNN has a single tanh activation function and suffers from vanishing/exploding gradients.

• LSTM RNN is better at capturing long-term dependencies in sequences.

• Simple RNN is simpler but struggles with

Lasso regression is a type of linear regression that uses L1 regularization to prevent overfitting by adding a penalty term to the loss function.

• Lasso regression helps in feature selection by shrinking the coefficients of less important features to zero.

• It is particularly useful when dealing with high-dimensional data where the number of features is much larger than the number of samples.

• The regularization parameter in...read more

Fine tuning a LLM model involves adjusting hyperparameters to improve performance.

• Perform grid search or random search to find optimal hyperparameters

• Use cross-validation to evaluate different hyperparameter combinations

• Regularize the model to prevent overfitting

• Adjust learning rate and batch size for better convergence

• Consider using techniques like early stopping to prevent overfitting

Deep learning is used over statistical models for complex, non-linear relationships in data.

• Deep learning can automatically learn hierarchical representations of data, capturing intricate patterns and relationships.

• Statistical models may struggle with high-dimensional data or non-linear relationships, where deep learning excels.

• Deep learning can handle unstructured data like images, audio, and text more effectively tha...read more

XGB is better than RF due to its ability to handle complex relationships and optimize performance.

• XGB uses gradient boosting which allows for better handling of complex relationships compared to RF

• XGB optimizes performance by using regularization techniques to prevent overfitting

• XGB is faster and more efficient in training compared to RF

• XGB allows for parallel processing which can speed up computation

• XGB has been shown...read more