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Krystian Kurek Mathematics English 118 cards

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Cards in this deck (118)

  1. 1Naive Bayes methodNaive Bayes method
  2. 2Proportional odds model6.pngProportional odds model
  3. 3Empirical risk7.pngEmpirical risk
  4. 4LDA vs logistic regression8.pngLDA vs logistic regression
  5. 5Polytomous logistic model9.pngPolytomous logistic model
  6. 6ridge10.pngridge
  7. 7Lasso11.pngLasso
  8. 8Existence of ML estimator12.pngExistence of ML estimator
  9. 9Iteratively Reweighted Least Squares13.pngIteratively Reweighted Least Squares
  10. 10Estimation of betas in logistic model14.pngEstimation of betas in logistic model
  11. 11aposteriori probabilities logistic regression15.pngaposteriori probabilities logistic regression
  12. 12logit16.pnglogit
  13. 13Stepclass direction: forward17.pngStepclass direction: forward
  14. 14QDA LDA number of parameters18.pngQDA LDA number of parameters
  15. 15Canonical vectors eigenvectors19.pngCanonical vectors eigenvectors
  16. 16between-groups covariance matrix20.pngbetween-groups covariance matrix
  17. 17Fisher’s geometric approach21.pngFisher’s geometric approach
  18. 18Spectral decomposition of Sigma_k22.pngSpectral decomposition of Sigma_k
  19. 19QDA – discriminant function23.pngQDA – discriminant function
  20. 20discriminant function LDA g>224.pngdiscriminant function LDA g>2
  21. 21within - class covariance matrix25.pngwithin - class covariance matrix
  22. 22discriminant function LDA26.pngdiscriminant function LDA
  23. 23Definition of conditional probability bayes rule nie wiem sam27.pngDefinition of conditional probability bayes rule nie wiem sam
  24. 24Bayes rule (Bayesian classifier)28.pngBayes rule (Bayesian classifier)
  25. 25False Discovery Rate29.pngFalse Discovery Rate
  26. 26PSR, recall30.pngPSR, recall
  27. 27Aim of classification analysis31.pngAim of classification analysis
  28. 28What we optimize in SVM separable case32.pngWhat we optimize in SVM separable case
  29. 29What we optimize in SVM non-separable case33.pngWhat we optimize in SVM non-separable case
  30. 30Lagrangian function in general case34.pngLagrangian function in general case
  31. 31which points are support vectors35.pngwhich points are support vectors
  32. 32SVM loss36.pngSVM loss
  33. 33Polynomial kernel37.pngPolynomial kernel
  34. 34Gaussian radial kernel RBF38.pngGaussian radial kernel RBF
  35. 35Laplace kernel39.pngLaplace kernel
  36. 36Sigmoid kernel40.pngSigmoid kernel
  37. 37Kernel SVM conditions41.pngKernel SVM conditions
  38. 38Regression SVM42.pngRegression SVM
  39. 39Regression task43.pngRegression task
  40. 40Entropy44.pngEntropy
  41. 41Ginni45.pngGinni
  42. 42Ginni interpretation46.pngGinni interpretation
  43. 43What we maximize in trees?47.pngWhat we maximize in trees?
  44. 44Nominal variables optimal order (in a tree)48.pngNominal variables optimal order (in a tree)
  45. 45Tree pruning49.pngTree pruning
  46. 461SE rule50.png1SE rule
  47. 47Precision51.pngPrecision
  48. 48Recall/True discovery rate52.pngRecall/True discovery rate
  49. 49False discovery rate53.pngFalse discovery rate
  50. 50Specificity54.pngSpecificity
  51. 51F score55.pngF score
  52. 52balanced accuracy56.pngbalanced accuracy
  53. 53Conditional misclassification error57.pngConditional misclassification error
  54. 54Unconditional misclassification error58.pngUnconditional misclassification error
  55. 55CV error59.pngCV error
  56. 56CV variance60.pngCV variance
  57. 57Bootstrap probability of not choosing element61.pngBootstrap probability of not choosing element
  58. 58Error boot-opt62.pngError boot-opt
  59. 59Error boot63.pngError boot
  60. 60Estimator 0.63264.pngEstimator 0.632
  61. 61LIFT65.pngLIFT
  62. 62Risk of decision rule66.pngRisk of decision rule
  63. 63What minimises Bayes risk?67.pngWhat minimises Bayes risk?
  64. 64kernel density estimation68.pngkernel density estimation
  65. 65General dependence h_n (in kernel estimation)69.pngGeneral dependence h_n (in kernel estimation)
  66. 66Silverman smoothing parameter70.pngSilverman smoothing parameter
  67. 67CV h_n - wybór71.pngCV h_n - wybór
  68. 68Kernel estimator with NN bandwidth72.pngKernel estimator with NN bandwidth
  69. 69Remarks on kNN73.pngRemarks on kNN
  70. 70Bagging (Bootstrap Aggregating)74.pngBagging (Bootstrap Aggregating)
  71. 71Weighted Bagging75.pngWeighted Bagging
  72. 72Bagging aposteriori probabilities76.pngBagging aposteriori probabilities
  73. 73Advantages and drawbacks of bagging77.pngAdvantages and drawbacks of bagging
  74. 74Bagging variance78.pngBagging variance
  75. 75Why is bagging useful79.pngWhy is bagging useful
  76. 76Jak wygląda cały adaboost80.pngJak wygląda cały adaboost
  77. 77weighted error of adaboost81.pngweighted error of adaboost
  78. 78gamma_c współczynnik w adaboost82.pnggamma_c współczynnik w adaboost
  79. 79Zmiana wag w adaboost83.pngZmiana wag w adaboost
  80. 80Predykcja adaboost84.pngPredykcja adaboost
  81. 81Adaboost error ostateczny (bound)85.pngAdaboost error ostateczny (bound)
  82. 82why adaboosting works86.pngwhy adaboosting works
  83. 83why adaboosting works (risk)87.pngwhy adaboosting works (risk)
  84. 84Property of loss function that tells if classification rule is Bayes classification function88.pngProperty of loss function that tells if classification rule is Bayes classification function
  85. 85Binomial boosting89.pngBinomial boosting
  86. 86L2boosting90.pngL2boosting
  87. 87Cały gradient boosting91.pngCały gradient boosting
  88. 88GB na czym budujemy regresje92.pngGB na czym budujemy regresje
  89. 89GB jak zmienia się funkcja klasyfikująca w m kroku93.pngGB jak zmienia się funkcja klasyfikująca w m kroku
  90. 90GB dla drzew94.pngGB dla drzew
  91. 91róznica między Greedy Forest a XGBoost95.pngróznica między Greedy Forest a XGBoost
  92. 92XGBoost emipircal risk for regression96.pngXGBoost emipircal risk for regression
  93. 93Random forest variable importance97.pngRandom forest variable importance
  94. 94Boruta98.pngBoruta
  95. 95MCFS99.pngMCFS
  96. 96Extremely Randomized Trees100.pngExtremely Randomized Trees
  97. 97RLDA i RQDA101.pngRLDA i RQDA
  98. 98Diagonal LDA102.pngDiagonal LDA
  99. 99NSC103.pngNSC
  100. 100Multiclass ridge regression104.pngMulticlass ridge regression
  101. 101Jakieś remarksy o lasso105.pngJakieś remarksy o lasso
  102. 102Elastic lasso106.pngElastic lasso
  103. 103Two step procedures107.pngTwo step procedures
  104. 104BIC108.pngBIC
  105. 105Addaptive lasso109.pngAddaptive lasso
  106. 106Information theory-based variable selection110.pngInformation theory-based variable selection
  107. 107ridge kernel method - regression111.pngridge kernel method - regression
  108. 108Parametric methods of estimating f - regression112.pngParametric methods of estimating f - regression
  109. 109Kernel estimator of regression function113.pngKernel estimator of regression function
  110. 110Locally linear estimator114.pngLocally linear estimator
  111. 111Splines115.pngSplines
  112. 112Crossvalidation for linear smoothers116.pngCrossvalidation for linear smoothers
  113. 113Mars forward step117.pngMars forward step
  114. 114Mars backward step118.pngMars backward step
  115. 115CV z GCV119.pngCV z GCV
  116. 116Additive model + Back-fitting algorithm120.pngAdditive model + Back-fitting algorithm
  117. 117Back-fitting algorithm for nonparametric logistic regression121.pngBack-fitting algorithm for nonparametric logistic regression
  118. 118Projection pursuit regression (PPR)122.pngProjection pursuit regression (PPR)

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Mathematics Flashcards on test, created by Krystian Kurek on 22/06/2022.
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Krystian Kurek
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