http://www.algolit.net/index.php?title=Linear_Regression_game&feed=atom&action=historyLinear Regression game - Revision history2024-03-28T22:23:00ZRevision history for this page on the wikiMediaWiki 1.31.14http://www.algolit.net/index.php?title=Linear_Regression_game&diff=11801&oldid=prevAn at 18:21, 4 June 20192019-06-04T18:21:59Z<p></p>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/6-Learners/Game_documentation Sources on Gitlab]</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the best-known and best-understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the best-known and best-understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td></tr>
</table>Anhttp://www.algolit.net/index.php?title=Linear_Regression_game&diff=11307&oldid=prevAn at 11:13, 14 March 20192019-03-14T11:13:54Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the <del class="diffchange diffchange-inline">most well </del>known and <del class="diffchange diffchange-inline">well </del>understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression <del class="diffchange diffchange-inline">is </del>is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the <ins class="diffchange diffchange-inline">best-</ins>known and <ins class="diffchange diffchange-inline">best-</ins>understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning <del class="diffchange diffchange-inline">practises</del>.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning <ins class="diffchange diffchange-inline">practices</ins>.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>----------------------------------------------------</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>----------------------------------------------------</div></td></tr>
</table>Anhttp://www.algolit.net/index.php?title=Linear_Regression_game&diff=11305&oldid=prevAn at 11:12, 14 March 20192019-03-14T11:12:49Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 11:12, 14 March 2019</td>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">by Algolit</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning practises.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning practises.</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">----------------------------------------------------</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
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</table>Anhttp://www.algolit.net/index.php?title=Linear_Regression_game&diff=10957&oldid=prevManetta at 18:52, 1 March 20192019-03-01T18:52:09Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 18:52, 1 March 2019</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">    </del>Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">    </del>By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning practises.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning practises.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Data_Workers]][[Category:Data_Workers_EN]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Data_Workers]][[Category:Data_Workers_EN]]</div></td></tr>
</table>Manettahttp://www.algolit.net/index.php?title=Linear_Regression_game&diff=10813&oldid=prevAn at 17:06, 28 February 20192019-02-28T17:06:33Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>     Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>     Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>     <del class="diffchange diffchange-inline">The basis for </del>this <del class="diffchange diffchange-inline">algorithm was invented by Francis Galton, </del>a <del class="diffchange diffchange-inline">cousin </del>of <del class="diffchange diffchange-inline">Charles Darwin </del>and <del class="diffchange diffchange-inline">an accomplished 19th century scientist</del>. <del class="diffchange diffchange-inline">He has often been criticized for his promotion </del>of <del class="diffchange diffchange-inline">“eugenics” (planned breeding of humans). While studying </del>the <del class="diffchange diffchange-inline">problem </del>of <del class="diffchange diffchange-inline">heredity – understanding how strongly </del>the <del class="diffchange diffchange-inline">characteristics </del>of <del class="diffchange diffchange-inline">one generation of living things manifested in the following generation, he was the first to define the linear regression slope</del>.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>     <ins class="diffchange diffchange-inline">By playing </ins>this <ins class="diffchange diffchange-inline">game you will realize that as a player you have </ins>a <ins class="diffchange diffchange-inline">lot </ins>of <ins class="diffchange diffchange-inline">decisions to make. You will experience what it means to create a coherent dataset, to decide what is in </ins>and <ins class="diffchange diffchange-inline">what is not in</ins>. <ins class="diffchange diffchange-inline">If all goes well, you will feel the urge to change your data in order to obtain better results. This is part </ins>of the <ins class="diffchange diffchange-inline">art </ins>of <ins class="diffchange diffchange-inline">approximation that is at </ins>the <ins class="diffchange diffchange-inline">basis </ins>of <ins class="diffchange diffchange-inline">all machine learning practises</ins>.</div></td></tr>
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</table>Anhttp://www.algolit.net/index.php?title=Linear_Regression_game&diff=10775&oldid=prevCristina at 13:52, 28 February 20192019-02-28T13:52:34Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>     The basis for this algorithm was invented by Francis Galton, a cousin of Charles Darwin and an accomplished 19th century scientist. He has often been criticized for his promotion of “eugenics” (planned breeding of humans). While studying the problem of heredity – understanding how strongly the characteristics of one generation of living things manifested in the following generation, he was the first to define the linear regression slope.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>     The basis for this algorithm was invented by Francis Galton, a cousin of Charles Darwin and an accomplished 19th century scientist. He has often been criticized for his promotion of “eugenics” (planned breeding of humans). While studying the problem of heredity – understanding how strongly the characteristics of one generation of living things manifested in the following generation, he was the first to define the linear regression slope.</div></td></tr>
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</table>Cristinahttp://www.algolit.net/index.php?title=Linear_Regression_game&diff=10658&oldid=prevAn: Created page with " Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attrac..."2019-02-21T19:30:07Z<p>Created page with " Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attrac..."</p>
<p><b>New page</b></p><div> Linear Regression is one of the most well known and well understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.<br />
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The basis for this algorithm was invented by Francis Galton, a cousin of Charles Darwin and an accomplished 19th century scientist. He has often been criticized for his promotion of “eugenics” (planned breeding of humans). While studying the problem of heredity – understanding how strongly the characteristics of one generation of living things manifested in the following generation, he was the first to define the linear regression slope.</div>An