Utilizing Statistical Tests for Comparing Machine Learning Algorithms
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not...
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| Autor*in: |
Hozan Khalid Hamarashid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Kurdistan Journal of Applied Research - Sulaimani Polytechnic University, 2018, 6(2021), 1 |
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| Übergeordnetes Werk: |
volume:6 ; year:2021 ; number:1 |
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Link aufrufen |
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| DOI / URN: |
10.24017/science.2021.1.8 |
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| Katalog-ID: |
DOAJ057184976 |
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| 520 | |a The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. | ||
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10.24017/science.2021.1.8 doi (DE-627)DOAJ057184976 (DE-599)DOAJceb1fd53930c43859354fde6646c6a08 DE-627 ger DE-627 rakwb eng T1-995 Hozan Khalid Hamarashid verfasserin aut Utilizing Statistical Tests for Comparing Machine Learning Algorithms 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. machine learning, machine learning assessment, statistical tests, machine learning algorithm, machine learning comparison. Technology (General) Science Q In Kurdistan Journal of Applied Research Sulaimani Polytechnic University, 2018 6(2021), 1 (DE-627)1047009935 24117706 nnns volume:6 year:2021 number:1 https://doi.org/10.24017/science.2021.1.8 kostenfrei https://doaj.org/article/ceb1fd53930c43859354fde6646c6a08 kostenfrei https://kjar.spu.edu.iq/index.php/kjar/article/view/630 kostenfrei https://doaj.org/toc/2411-7684 Journal toc kostenfrei https://doaj.org/toc/2411-7706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 |
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10.24017/science.2021.1.8 doi (DE-627)DOAJ057184976 (DE-599)DOAJceb1fd53930c43859354fde6646c6a08 DE-627 ger DE-627 rakwb eng T1-995 Hozan Khalid Hamarashid verfasserin aut Utilizing Statistical Tests for Comparing Machine Learning Algorithms 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. machine learning, machine learning assessment, statistical tests, machine learning algorithm, machine learning comparison. Technology (General) Science Q In Kurdistan Journal of Applied Research Sulaimani Polytechnic University, 2018 6(2021), 1 (DE-627)1047009935 24117706 nnns volume:6 year:2021 number:1 https://doi.org/10.24017/science.2021.1.8 kostenfrei https://doaj.org/article/ceb1fd53930c43859354fde6646c6a08 kostenfrei https://kjar.spu.edu.iq/index.php/kjar/article/view/630 kostenfrei https://doaj.org/toc/2411-7684 Journal toc kostenfrei https://doaj.org/toc/2411-7706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 |
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10.24017/science.2021.1.8 doi (DE-627)DOAJ057184976 (DE-599)DOAJceb1fd53930c43859354fde6646c6a08 DE-627 ger DE-627 rakwb eng T1-995 Hozan Khalid Hamarashid verfasserin aut Utilizing Statistical Tests for Comparing Machine Learning Algorithms 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. machine learning, machine learning assessment, statistical tests, machine learning algorithm, machine learning comparison. Technology (General) Science Q In Kurdistan Journal of Applied Research Sulaimani Polytechnic University, 2018 6(2021), 1 (DE-627)1047009935 24117706 nnns volume:6 year:2021 number:1 https://doi.org/10.24017/science.2021.1.8 kostenfrei https://doaj.org/article/ceb1fd53930c43859354fde6646c6a08 kostenfrei https://kjar.spu.edu.iq/index.php/kjar/article/view/630 kostenfrei https://doaj.org/toc/2411-7684 Journal toc kostenfrei https://doaj.org/toc/2411-7706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 |
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10.24017/science.2021.1.8 doi (DE-627)DOAJ057184976 (DE-599)DOAJceb1fd53930c43859354fde6646c6a08 DE-627 ger DE-627 rakwb eng T1-995 Hozan Khalid Hamarashid verfasserin aut Utilizing Statistical Tests for Comparing Machine Learning Algorithms 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. machine learning, machine learning assessment, statistical tests, machine learning algorithm, machine learning comparison. Technology (General) Science Q In Kurdistan Journal of Applied Research Sulaimani Polytechnic University, 2018 6(2021), 1 (DE-627)1047009935 24117706 nnns volume:6 year:2021 number:1 https://doi.org/10.24017/science.2021.1.8 kostenfrei https://doaj.org/article/ceb1fd53930c43859354fde6646c6a08 kostenfrei https://kjar.spu.edu.iq/index.php/kjar/article/view/630 kostenfrei https://doaj.org/toc/2411-7684 Journal toc kostenfrei https://doaj.org/toc/2411-7706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 |
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The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. |
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The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. |
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The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. |
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