Empower Data Scientist Interview Questions and Answers

Precision = TP / (TP + FP), Recall = TP / (TP + FN)

• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class

• Example: If a model predicts 100 positive cases, out of which 80 are actually positive, precision = 80/100, recall = 80/total actual positive cases

R2 measures the proportion of variance explained by the model, while adjusted R2 adjusts for the number of predictors in the model.

• R2 is the proportion of variance in the dependent variable that is predictable from the independent variables.

• Adjusted R2 penalizes the addition of unnecessary predictors in the model, providing a more accurate measure of the model's goodness of fit.

• R2 can increase even when adding irreleva...read more

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

Precision = TP / (TP + FP), Recall = TP / (TP + FN)

• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class

• Example: If a model predicts 100 positive cases, out of which 80 are actually positive, precision = 80/100, recall = 80/total actual positive cases

R2 measures the proportion of variance explained by the model, while adjusted R2 adjusts for the number of predictors in the model.

• R2 is the proportion of variance in the dependent variable that is predictable from the independent variables.

• Adjusted R2 penalizes the addition of unnecessary predictors in the model, providing a more accurate measure of the model's goodness of fit.

• R2 can increase even when adding irreleva...read more