ML3

Edit Answers
123456789101112131415161718192021
Across
  1. 4. A simple model or method used as a reference point for comparing the performance of more complex models
  2. 7. A non-parametric measure of statistical dependence between two variables, calculated over the ranked values
  3. 9. A measure of how frequently the itemset appears in the dataset
  4. 11. A kind of correlation which quantifies the degree to which two variables are linearly related or associated
  5. 14. An open-source software library developed by Yandex
  6. 15. A phenomenon occurs when a model has not learned the patterns in the training data well and is unable to generalize well on the new data
  7. 16. Converting categorical data (text or labels) into a numerical format that can be used as input for machine learning algorithms
  8. 19. A numerical value that an agent receives as feedback from the environment after taking an action
  9. 20. A concept that measures the amount of uncertainty or disorder in a set of data
  10. 21. An ensemble machine learning algorithm that can be used in a wide variety of classification and regression tasks
Down
  1. 1. A kind of analysis which is also known as the 80/20 rule or the principle of factor sparsity
  2. 2. A technique in machine learning and data analysis that involves grouping similar data points together based on certain criteria
  3. 3. A kind of function which is an S-shaped curve that maps any real-valued number to a value between 0 and 1
  4. 5. A phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined
  5. 6. A kind of matrix also known as cost matrix or misclassification cost matrix
  6. 8. A metric used to evaluate the performance of a classification model
  7. 9. A kind of diagram showing the flow of resources or information from one set of entities to another
  8. 10. A one-dimensional form of data
  9. 12. A kind of regression modeling technique that assumes a linear relationship between the independent variables and the dependent variable
  10. 13. An ensemble learning technique in machine learning where multiple weak learners (models that perform slightly better than random chance) are combined to create a strong learner
  11. 17. A phenomenon in machine learning where a model learns the training data too well, capturing noise and random fluctuations rather than the underlying patterns
  12. 18. A kind of sampling where every member of the population has an equal chance of being included in the sample