A logarithmic scale contour plot of the error surface of a linear regression with two parameters. Note that the fit is quite poor, as seen in the large range of paramter combinations with similar errors.
Data source: Auto MPG Data Set, Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
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| .axis path, | |
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| .tile { | |
| shape-rendering: crispEdges; | |
| } | |
| .dot { | |
| stroke: #000; | |
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| </style> | |
| <body> | |
| <script src="http://d3js.org/d3.v3.min.js"></script> | |
| <script> | |
| var margin = {top: 20, right: 20, bottom: 30, left: 30}, | |
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| height = 500 - margin.top - margin.bottom; | |
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| .scale(x) | |
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| .attr("height", height + margin.top + margin.bottom) | |
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| .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); | |
| d3.json("AutoMpg.json", function(error, data){ | |
| var surface = []; | |
| for (var i = 0; i < 200; i ++){ | |
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| surface.push({'x': gridX(i),'y': gridY(j), 'e': calculateError(gridX(i), gridY(j))}); | |
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| .append("text") | |
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| .style("text-anchor", "end") | |
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| for (var i = 0; i < data.length; i++){ | |
| error += Math.pow(data[i].mpg - ((b1 * data[i].horsepower) + b0), 2); | |
| } | |
| return error / data.length; | |
| } | |
| }); | |
| </script> |
