HRS GmbH Data Scientist Interview Questions and Answers

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.

• It assumes a linear relationship between the variables

• It is used to predict the value of the dependent variable based on the independent variable(s)

• It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)

• It is commo...read more

ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Random Forest

• Naive Bayes

• Decision Trees

• Linear Regression

• Logistic Regression

• Neural Networks

• Gradient Boosting

• Clustering Algorithms (K-Means, Hierarchical)

• Association Rule Learning (Apriori)

• Dimensionality Reduction Algorithms (PCA, LDA)

• Reinf

Overfitting occurs when a model learns the training data too well, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

• Overfitting: Model is too complex, fits noise in the training data, performs poorly on new data

• Underfitting: Model is too simple, fails to capture underlying patterns in the data, performs poorly on both training and new...read more

LLM models, or Language Model Models, are a type of machine learning model that focuses on predicting the next word in a sequence of words.

• LLM models are commonly used in natural language processing tasks such as text generation, machine translation, and speech recognition.

• They are trained on large amounts of text data to learn the relationships between words and predict the most likely next word in a given context.

• Exa...read more