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ML Algorithms Suite Calculator

Complete ML algorithm analysis. KNN, decision trees, random forests, SVMs, clustering, and gradient boosting parameters and complexity.

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Step 1 of 9

How to Use This Calculator
  1. Follow the step-by-step wizard — complete each step before moving to the next. This guided approach ensures you provide all the necessary information in a logical order.
  2. Algorithm Type — Calculation mode to use. Choose from: K-Nearest Neighbors, Decision Trees / Random Forest, Support Vector Machines, Clustering (K-Means/DBSCAN), Gradient Boosting. default: 0.
  3. k (Neighbors) — enter a numeric value. default: 5.
  4. Training Samples — enter a numeric value. default: 1,000.
  5. Features — enter a numeric value. default: 10.
  6. Distance Metric — enter your preferred option from the dropdown. Choose from: Euclidean, Manhattan, Minkowski. default: 0.
  7. Max Depth — enter a numeric value. default: 10.
  8. Min Samples per Leaf — enter a numeric value. default: 2.
  9. Number of Trees (RF) — enter a numeric value. default: 100.
  10. Features — enter a numeric value. default: 10.
  11. Training Samples — enter a numeric value. default: 1,000.
  12. Training Samples — enter a numeric value. default: 1,000.
  13. Features — enter a numeric value. default: 10.
  14. Kernel — SVM kernel function type. Choose from: Linear, RBF (Gaussian), Polynomial. default: 1.
  15. C (Regularization) — enter a numeric value. default: 1.
  16. Algorithm — enter your preferred option from the dropdown. Choose from: K-Means, DBSCAN. default: 0.
  17. Number of Clusters (k) — enter a numeric value. default: 5.
  18. Samples — enter a numeric value. default: 1,000.
  19. Features — enter a numeric value. default: 10.
  20. Epsilon (DBSCAN) — enter a numeric value. default: 0.5.
  21. Min Points (DBSCAN) — enter a numeric value. default: 5.
  22. Number of Trees — enter a numeric value. default: 100.
  23. Max Depth — enter a numeric value. default: 6.
  24. Learning Rate — enter a numeric value. default: 0.1.
  25. Subsample Ratio — enter a numeric value. default: 0.8.
  26. Training Samples — enter a numeric value. default: 10,000.
  27. Features — enter a numeric value. default: 50.
  28. Once all inputs are set, review your results in the Results panel. Here's what each output means:
  29. Algorithm — your calculated result. This is the primary result of this calculator.
  30. Optimal k (√n) — shown as a numeric value.
  31. Predict Complexity — your calculated result.
  32. Memory (MB) — shown as a numeric value.
  33. Curse of Dimensionality — your calculated result.
  34. RF Train Complexity — your calculated result.
  35. Forest Memory (MB) — shown as a numeric value.
  36. Overfitting Risk — your calculated result.
  37. Kernel — your calculated result.
  38. Train Complexity — your calculated result.
  39. Support Vectors (%) — shown as a percentage.
  40. Feasibility — your calculated result.
  41. Complexity — your calculated result.
  42. Est. Silhouette — shown as a numeric value.
  43. Effective Iterations — shown as a numeric value.
  44. Regularization — your calculated result.
  45. Memory (MB) — shown as a numeric value.
  46. View the Algorithm Performance below for a visual breakdown of how the numbers relate to each other.
  47. Check the Algorithm Analysis for a detailed row-by-row breakdown. This is useful for spotting trends or finding values at specific points.
  48. Explore the related calculators below if you need deeper analysis or want to approach this topic from a different angle.
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Formula

KNN: O(nd) | RF: O(Tnd log n) | SVM: O(n²d) to O(n³) | GB: O(Tnd 2^depth)

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