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"# Summary\n",
"\n",
"We've introduced the constant model: a model that uses a single number to\n",
"summarize all of the entries in a dataset. To fit the constant model, we chose\n",
"a loss function that measured how well a given constant fits a data value, and\n",
"we computed the average loss over all of the data values. We saw that depending\n",
"on the choice of loss function, we get a different minimizing value.\n",
"We found that the mean minimizes the average squared error (MSE) and the median\n",
"minimizes the average absolute error (MAE). We also discussed how we can\n",
"incorporate context and knowledge of our problem to pick loss functions.\n",
"\n",
"The idea of fitting models through loss minimization ties simple summary\n",
"statistics---like the mean, median, and mode---to more complex modeling\n",
"situations. The steps we took to model our data apply to many modeling\n",
"scenarios:\n",
"\n",
"1. Select the form of a model (such as the constant model)\n",
"2. Select a loss function (such as absolute error)\n",
"3. Fit the model by minimizing the average loss for the data\n",
"\n",
"For the rest of this book, all of our modeling techniques expand upon one or\n",
"more of these steps. We introduce new models (1), new loss functions (2), and\n",
"new techniques for minimizing loss (3)."
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