Top 10 Regression Interview Questions and Answers

Regression predicts continuous values while classification predicts categorical values.

• Regression is used to predict a continuous output variable based on one or more input variables.

• Classification is used to predict a categorical output variable based on one or more input variables.

• Regression algorithms include linear regression, polynomial regression, and logistic regression.

• Classification algorithms include decision trees, random forests, and support vector machines.

• Exampl...read more

Correlation measures the strength of a linear relationship between two variables, while regression predicts the value of one variable based on the value of another.

• Correlation measures the degree of association between two variables.

• Regression predicts the value of one variable based on the value of another.

• Correlation does not imply causation.

• Regression can be used to identify the relationship between variables and make predictions.

• Correlation coefficient ranges from -1 to 1...read more

Regression and correlation are statistical methods used to analyze the relationship between two variables.

• Regression is used to predict the value of one variable based on the value of another variable.

• Correlation measures the strength and direction of the relationship between two variables.

• Regression and correlation are often used in industrial engineering to analyze data and make predictions.

• Examples of regression and correlation include analyzing the relationship between pr...read more

STLC stands for Software Testing Life Cycle. Regression testing is the process of testing changes made to the software to ensure that existing functionalities still work.

• STLC is a process followed by QA engineers to ensure that software is thoroughly tested before release

• It includes planning, designing, executing, and reporting of tests

• Regression testing is a type of testing that is performed to ensure that changes made to the software do not affect existing functionalities

• It...read more

Logistic regression is used for classification while linear regression is used for regression. Threshold is decided based on the problem.

• Logistic regression predicts the probability of an event occurring, while linear regression predicts a continuous outcome.

• Logistic regression uses a sigmoid function to map the predicted values between 0 and 1.

• Linear regression uses a linear equation to model the relationship between the independent and dependent variables.

• The threshold in l...read more

Regression is a statistical technique used to understand the relationship between a dependent variable and one or more independent variables. Multicollinearity occurs when independent variables in a regression model are highly correlated.

• Regression helps in predicting the value of the dependent variable based on the values of independent variables.

• Multicollinearity can lead to issues in interpreting the coefficients of the independent variables in a regression model.

• Detecting...read more

The independent variables for predicting strawberry harvest through regression are weather conditions, soil quality, fertilizers used, and planting density.

• Weather conditions such as temperature, humidity, and rainfall affect the growth and yield of strawberries.

• Soil quality, including pH level, nutrient content, and texture, can impact the health and productivity of the plants.

• Fertilizers used, such as nitrogen, phosphorus, and potassium, can affect the growth and yield of t...read more

Different cost functions for regression are used to measure the error between predicted and actual values.

• Common cost functions include Mean Squared Error (MSE), Mean Absolute Error (MAE), and Huber Loss.

• MSE penalizes larger errors more heavily, while MAE treats all errors equally.

• Huber Loss is a combination of MSE and MAE, providing a balance between robustness and sensitivity to outliers.

Regression is a statistical method to predict continuous outcomes, while classification is used to predict categorical outcomes.

• Regression is used when the target variable is continuous, such as predicting house prices based on features like size and location.

• Classification is used when the target variable is categorical, like predicting whether an email is spam or not based on its content.

• Regression models include linear regression, polynomial regression, and logistic regres...read more

Metrics for regression include Mean Squared Error, R-squared, and Mean Absolute Error. For classification, metrics include Accuracy, Precision, Recall, and F1 Score.

• Regression metrics: Mean Squared Error, R-squared, Mean Absolute Error

• Classification metrics: Accuracy, Precision, Recall, F1 Score