| Title | : | Machine Learning vs Statistical Modeling |
| Lasting | : | 4.06 |
| Date of publication | : | |
| Views | : | 58 rb |
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This is nonsense Pls don't follow it Comment from : K S |
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who created the statistical model? Comment from : Luis Fredy Huachaca Vargas |
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why do you sound like Caitlyn Jenner? Comment from : Marcos Perez |
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Biased I think you should learn more about the basic of algorithm behind machine learning models Comment from : Meri Andani |
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This is so biased Comment from : Michael Glenn |
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Are you kidding me? Algorithms based on mathematics too Based on this video you know nothing about machine learning or even computer science itself Lol Comment from : azhanydotxyz |
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Why the output of a standard regression (with p-values, coefficients, etcetera) doesn’t look like a machine learning regression output? Comment from : miguel medina |
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Why the output of a standard regression (with p-values, coefficients, etcetera) doesn’t look like a machine learning regression output? Comment from : miguel medina |
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This video is completely misinforming the viewer Machine Learning is solidly based in statistics, but contextualizes the objectives in a descriptive rather than inferential way Comment from : michiNYC |
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This is an, if not erroneous, incomplete presentation of machine learning vs statistical modeling Designed experiments is a vital part, if not a more complete form of statistical modelling than predictive statistical modeling It allows you to model cause and effects and establish a base for mechanistic modeling If you do not know why you get the prediction you get from the ML algorithms, you can "drill down" with a set of designed experiments A/B testing is primitive but very commonly used designed experiment eg in web design Thus, it is reasonable to assume that the most productive development environments will include both the machine learning toolset and the statistical modelling toolset This especially if you are working in a cross-disciplinarian environment such as the auto, power or aerospace industries Environments that also frequently use first principle simulation tools You may assume that all these tools will merge into a powerful integrated toolset Or? Comment from : tmusic99 |
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I don't agree brStatistical modelling AND machine learning are looking for relationships between variablesbrThere are many assumptions one must make when using machine learning techniques and you must choose the correct modelbrA statistical model such as lm can be used on 'big data' and can be just as predictive as a machine learning algorithm depending on the data Comment from : Michael Sinclair |
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Lucid Explanation Comment from : Zubair Hasan |
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