Estando en una cafetería, repasé los conceptos de Programación Orientada a Objetos para una entrevista, (es una pregunta obligada).
Haciendo una analogía, comparé la experiencia que se vive en una cafetería común con conceptos de POO, y este fue el resultado 🧵👇
The time complexity of 10 popular ML algorithms.
Understanding the run time of ML algorithms is important because it helps us:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions that allow us to use an algorithm.
For instance, using SVM or t-SNE on large datasets is infeasible because of their polynomial relation with data size.
Similarly, using OLS on a high-dimensional dataset makes no sense because its run-time grows cubically with total features.
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Over to you: Can you tell the inference run-time of KMeans Clustering?