Frosi Exam: Chapter 1
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- What are the major steps of agriculture?
- What is one possible solution to exploit the huge amounts of data in the agrifood industry? What should one do to utilize the data?
- What is the main action companies can use to organize this data? What can we do with it?
- What are the 5 steps of data processing?
- How is a dataset formed? What are the objects called?
- Classification of data depending on their values? Explain them
- Would you use raw data directly in ML or something else?
- Which type of machine learning model does regression belong to? Elaborate.
- Is there a benefit to supervised vs unsupervised learning?
- Describe frequency, mode, mean and median.
- What are possible ways to visual data in plots?
- How can machine learning methods actually be classified? Explain and give example of 3 types in the real world?
- Possible ways to visualize data in plots? List them.
- How can ML methods be classified? Make an example of supervised and unsupervised in a real world application in agri-food chain.
- Which is the task that takes up the most amount of time in the data analysis process? Why is data cleaning important?
- What is One-Hot Encoding?
- What is Integer/Label Encoding?
- What is the Agenda 2030?
- What are the solutions to Agriculture 4.0?
- What are the Feature Types?
- How do you transform raw data to be used for data analysis algorithms?
- What type of encoding would you use for nominal features? Why?
- What type of encoding would you use for ordinal features? Why?
- Why is some data missing or not defined correctly?
- How is missing data represented?
- What are the types of missing values?
- What imputation models can you use to assign new values to continuous features?
- What imputation models can you use to assign new values to categorical features?
- What tools would you use during the preliminary exploration phase of data?
- Why would you use statistics and visualization tools? In order to?
- What does mean and median measure of a dataset?
- What does range and variance measure of a dataset?
- What is an outlier?
- How would you detect outliers?
- What is a Z-score?
- What is IQR?
- How would you filter out outliers?
- What are the 2 types of normalization?
- What is machine learning?
- What are the types of machine learning paradigms?
- What is the goal of supervised learning? With regression?
- What is the goal of supervised learning in regards to classification?
- What is the goal of unsupervised with clustering?