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°²³]§Ú̦³¤@Ó¸ê®Æ¶° $D$¡A¥]§t¤F $n$ Ó¿é¤J¿é¥X¹ï¡G $$ D=\{ (x_1, y_1), (x_2, y_2), ..., (x_n, y_n) \} $$
§ÚÌ¥i¥H©w¸q¤@Ó¼Ò«¬ $f()$ ¹ï©ó¦¹¸ê®Æ¶° $D$ ªº RMSE (root-mean-squared error): $$ rmse(f, D) = \sum_{i=1}^n |y_i-f(x_i)|^2 $$
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polyfit «ü¥O©Ò¦^¶Çªº¦h¶µ¦¡¥i¥Hªí¥Ü¦p¤U¡G $$ f = polyfit(D, r)$$¨ä¤¤ $D$ ¬O¥Î©óÀÀ¦Xªº¸ê®Æ¶°¡A$r$ «h¬O¦h¶µ¦¡ªº¦¸¼Æ¡C
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- Training RMSE: $t(r)=\sum_{i=1}^n rmse(f_i, D-\{(x_i, y_i)\})$¡A¨ä¤¤ $f_i$ ¬O¥Ñ¸ê®Æ¶° $D-\{(x_i, y_i)\}$ ©Ò«Ø¥ßªº¼Ò«¬¡C¡]¥t¤@ºØªí¥Üªk¡G$f_i=polyfit(D-\{(x_i, y_i)\}, r)$.¡^
- Validating RMSE: $v(r)=\sum_{i=1}^n rmse(f_i, \{(x_i, y_i)\})$¡A¨ä¤¤ $f_i$ ¬O¥Ñ¸ê®Æ¶° $D-\{(x_i, y_i)\}$ ©Ò«Ø¥ßªº¼Ò«¬¡C¡]¥t¤@ºØªí¥Üªk¡G$f_i=polyfit(D-\{(x_i, y_i)\}, r)$.¡^
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