Podcast
Questions and Answers
What is the purpose of exploratory data analysis (EDA)?
What is the purpose of exploratory data analysis (EDA)?
- To build up a general and detailed picture of the data (correct)
- To save data in a universal format
- To balance target variable classes
- To select the most important features for modeling
What are the types of visualizations used in EDA?
What are the types of visualizations used in EDA?
- Pie charts, line graphs, and scatter plots
- Box plots, area charts, and tree maps
- Histograms, bar charts, and heat maps
- Univariate, bivariate, and multivariate (correct)
What are some examples of univariate analysis?
What are some examples of univariate analysis?
- Tests for white noise, dimension reduction, clustering
- Descriptive statistics, one-sample tests, tests for autocorrelation (correct)
- Regression analysis, hypothesis testing, ANOVA
- Time series analysis, survival analysis, decision trees
What is data imputation?
What is data imputation?
Which type of variables can logistic regression be used to predict?
Which type of variables can logistic regression be used to predict?
What is the primary use of logistic regression from an econometric perspective?
What is the primary use of logistic regression from an econometric perspective?
Why is interpreting logistic regression results more difficult than interpreting linear regression results?
Why is interpreting logistic regression results more difficult than interpreting linear regression results?
What is the focus of logistic regression in this course?
What is the focus of logistic regression in this course?
What is recommended for learning the principles of logistic regression from an econometric perspective?
What is recommended for learning the principles of logistic regression from an econometric perspective?
What is the cost function used for logistic regression?
What is the cost function used for logistic regression?
What is the purpose of the link function in GLM?
What is the purpose of the link function in GLM?
What is the advantage of using logistic regression over linear regression?
What is the advantage of using logistic regression over linear regression?
What is the difference between binary logistic regression and multinomial logistic regression?
What is the difference between binary logistic regression and multinomial logistic regression?
What is the purpose of the odds ratio in logistic regression?
What is the purpose of the odds ratio in logistic regression?
What is the purpose of feature engineering during the ETL process?
What is the purpose of feature engineering during the ETL process?
What is the purpose of feature engineering after the ETL process?
What is the purpose of feature engineering after the ETL process?
What is the purpose of scaling to a range in numeric variable transformations?
What is the purpose of scaling to a range in numeric variable transformations?
What is the purpose of clipping (winsorization) in numeric variable transformations?
What is the purpose of clipping (winsorization) in numeric variable transformations?
What is the main challenge in feature engineering after the ETL process?
What is the main challenge in feature engineering after the ETL process?
What is the purpose of multinomial logistic regression?
What is the purpose of multinomial logistic regression?
What is logistic regression?
What is logistic regression?
What is the sigmoid function?
What is the sigmoid function?
What are the useful properties of the logistic function?
What are the useful properties of the logistic function?
What course is used in the text to present the concepts, mathematical foundations, and interpretation of logistic regression?
What course is used in the text to present the concepts, mathematical foundations, and interpretation of logistic regression?
What is the advantage of machine learning over classical econometrics in terms of feature engineering?
What is the advantage of machine learning over classical econometrics in terms of feature engineering?
What are some examples of super powerful encoders mentioned in the text?
What are some examples of super powerful encoders mentioned in the text?
What is a cautionary note given by the author regarding feature engineering in financial problems?
What is a cautionary note given by the author regarding feature engineering in financial problems?
What types of interactions can we look for between variables during feature engineering?
What types of interactions can we look for between variables during feature engineering?
What are some techniques for dealing with missing values in a dataset?
What are some techniques for dealing with missing values in a dataset?
When should variables/columns with missing values be removed from a dataset?
When should variables/columns with missing values be removed from a dataset?
What is feature engineering?
What is feature engineering?
What is one way to fill in missing values for time series variables?
What is one way to fill in missing values for time series variables?
What is one multivariate technique for dealing with missing values in a dataset?
What is one multivariate technique for dealing with missing values in a dataset?
What is the purpose of a problem statement worksheet in machine learning projects?
What is the purpose of a problem statement worksheet in machine learning projects?
What are the elements of a data preparation process in machine learning projects?
What are the elements of a data preparation process in machine learning projects?
What is the role of consulting firms in machine learning projects?
What is the role of consulting firms in machine learning projects?
What should be applicable later on the test set in a machine learning project?
What should be applicable later on the test set in a machine learning project?
