GitHub / Sarthak-Mohapatra / Classification-of-GPU-Run-as-high-or-low-time-consuming-using-various-classification-Algorithms.
As part of this project, various classification algorithms like SVM, Decision Trees and XGBoost was used to classify a GPU Run as high or low time consuming process. The main purpose of this project is to test and compare the predictive capabilities of different classification algorithms
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PURL: pkg:github/Sarthak-Mohapatra/Classification-of-GPU-Run-as-high-or-low-time-consuming-using-various-classification-Algorithms.
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Forks: 0
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 4 MB
Dependencies parsed at: Pending
Created at: over 5 years ago
Updated at: over 1 year ago
Pushed at: over 5 years ago
Last synced at: over 1 year ago
Topics: classification-algorithms, cross-validation, decision-trees, gpu-kernel-performance, k-fold-cross-validation, machine-learning-algorithms, matplotlib, matrix, matrix-multiplication, numpy, pandas, performance-analysis, python, scikit-learn, seaborn, svm, svm-kernel, xgboost-algorithms