ML Algorithms Suite Calculator

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

Progress0%

Algorithm Type

k (Neighbors)

Training Samples

How to Use This Calculator

Select the algorithm type (classification, regression, clustering) to filter relevant algorithms.
Input your dataset size, feature count, and target variable type.
Review the recommended algorithms ranked by suitability for your problem type.
Set hyperparameter ranges for the top candidate algorithms.
Use the complexity and interpretability ratings to balance performance with explainability requirements.

What each input means

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

Identify Input Parameters
4 parameters
Algorithm Type = 0, k (Neighbors) = 5, Training Samples = 1000, Features = 10 = 26 input(s) provided
Calculate Optimal k
Optimal k
32 = 32
Calculate Predict Complexity
Predict Complexity
O(n×d) = O(10,000) = O(n×d) = O(10,000)

Engine last updated August 5, 2026. Checked against 5 independently-derived tests — how we verify calculators.

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