1 00:00:00,133 --> 00:00:00,666 Okay. 2 00:00:00,666 --> 00:00:05,300 So now let's quickly do the same for the SDR results in high resolution. 3 00:00:05,333 --> 00:00:07,866 So here we're going to create a new code cell. 4 00:00:07,866 --> 00:00:09,733 And again let's do it efficiently. 5 00:00:09,733 --> 00:00:12,700 We're going to go to our polynomial regression implementation. 6 00:00:12,700 --> 00:00:15,733 Scroll down here to find the code for the high resolution. 7 00:00:16,033 --> 00:00:21,000 Select it copy it and go back to our SDR implementation. 8 00:00:21,333 --> 00:00:22,133 These are here. 9 00:00:22,133 --> 00:00:25,133 And now again let's see what we have to replace. 10 00:00:25,233 --> 00:00:25,533 All right. 11 00:00:25,533 --> 00:00:30,600 So first let's make the obvious change again polynomial regression by SVR. 12 00:00:30,600 --> 00:00:33,033 And then same. Let's check row by row. 13 00:00:33,033 --> 00:00:35,266 So in the first two rows what do we have to do. 14 00:00:35,266 --> 00:00:38,700 Well of course we have to reverse the scaling for the two axis. 15 00:00:38,700 --> 00:00:41,200 Here. Right. Because x is still scaled. 16 00:00:41,200 --> 00:00:43,200 So we have to put that back in the original scale 17 00:00:43,200 --> 00:00:46,500 so that we can get x grid in the original scale as well. 18 00:00:46,666 --> 00:00:47,300 Okay. 19 00:00:47,300 --> 00:00:51,300 So let's again call our c x scale object 20 00:00:51,300 --> 00:00:55,400 from which we call again to inverse underscore transform. 21 00:00:55,733 --> 00:00:58,733 There we go. Method applied to x. 22 00:00:58,800 --> 00:01:00,266 And then same for the second x. 23 00:01:00,266 --> 00:01:03,966 Here we call our c x scaler object 24 00:01:03,966 --> 00:01:08,000 from which we call the inverse underscore transform. 25 00:01:08,033 --> 00:01:10,800 There we go. Method apply to x. 26 00:01:10,800 --> 00:01:13,733 Okay great I think we're good for the first row. 27 00:01:13,733 --> 00:01:15,066 Now second row. 28 00:01:15,066 --> 00:01:19,200 The second row is actually fine because x grid is now back into the original shape. 29 00:01:19,200 --> 00:01:21,066 So we don't have to change anything here. 30 00:01:21,066 --> 00:01:24,066 So then row number three. Well here same. 31 00:01:24,066 --> 00:01:27,900 We have to apply the inverse transformation on both the inputs x 32 00:01:27,900 --> 00:01:28,766 and the output y. 33 00:01:28,766 --> 00:01:33,733 So let's do that starting with x we call our SC x scatter object 34 00:01:33,733 --> 00:01:39,300 from which we call the inverse transform method apply to x. 35 00:01:39,600 --> 00:01:40,433 And then same. 36 00:01:40,433 --> 00:01:46,033 Here we call our SC y square object from which we applied inverse transform method. 37 00:01:46,033 --> 00:01:48,633 Here we go apply to y. Excellent. 38 00:01:48,633 --> 00:01:51,000 So now I think we're good with row number three. 39 00:01:51,000 --> 00:01:53,000 And now let's move on to row number four. 40 00:01:53,000 --> 00:01:54,966 So in row number four x is fine. 41 00:01:54,966 --> 00:01:56,866 It's already back into the original scale. 42 00:01:56,866 --> 00:01:59,366 But the predictions are not fine. 43 00:01:59,366 --> 00:02:01,100 Of course we're going to replace this whole 44 00:02:01,100 --> 00:02:03,700 prediction here of the polynomial regression by. 45 00:02:03,700 --> 00:02:08,233 Actually let's make it efficient by the prediction we got here. 46 00:02:08,233 --> 00:02:11,033 But of course we need to replace x by x grid. 47 00:02:11,033 --> 00:02:12,500 So let's take all this. 48 00:02:12,500 --> 00:02:16,566 Let's scroll back down and let's replace this line rect 49 00:02:16,566 --> 00:02:21,733 to predict fully rect fit transform X grid by our SVR prediction, 50 00:02:21,733 --> 00:02:25,200 after which we replace of course x by x grid. 51 00:02:25,733 --> 00:02:27,466 Okay, do you think we're done now? 52 00:02:27,466 --> 00:02:29,566 Well, actually, bad news, we're not done yet. 53 00:02:29,566 --> 00:02:31,433 There's one final thing we need to do. 54 00:02:31,433 --> 00:02:32,633 Do you see what it is? 55 00:02:32,633 --> 00:02:36,266 Well, you notice that here we apply the predict method to X grid. 56 00:02:36,266 --> 00:02:40,266 And x grid is not scaled because here we apply the inverse transform of x 57 00:02:40,466 --> 00:02:41,500 when making x grid. 58 00:02:41,500 --> 00:02:44,933 So here we just need to apply one last time I promise 59 00:02:45,100 --> 00:02:50,633 the x object next grid so that we get the scaled values of x grid, so that. 60 00:02:50,633 --> 00:02:54,400 Therefore the predict method can make its predictions in the right format. 61 00:02:54,633 --> 00:02:55,200 All right. 62 00:02:55,200 --> 00:02:57,900 So that was last little difficulty here. So let's do this. 63 00:02:57,900 --> 00:03:00,300 Let's call again for the last time. 64 00:03:00,300 --> 00:03:04,400 Our scaler object X from which we call that 65 00:03:04,400 --> 00:03:07,400 there it is transform method 66 00:03:07,400 --> 00:03:10,166 applied to this time x grit. 67 00:03:10,166 --> 00:03:12,633 Okay. So now I think we're really done. 68 00:03:12,633 --> 00:03:14,966 The only way to check this is by running this cell. 69 00:03:14,966 --> 00:03:15,933 And let's see what we get. 70 00:03:15,933 --> 00:03:21,866 We get the beautiful curve in high resolution of the SVR model. 71 00:03:21,866 --> 00:03:24,000 So congratulations. 72 00:03:24,000 --> 00:03:25,266 That was a pretty tough one. 73 00:03:25,266 --> 00:03:28,300 And if you did it all by yourself first well double congrats. 74 00:03:28,966 --> 00:03:29,433 Okay. 75 00:03:29,433 --> 00:03:32,066 So now we're going to move on to the next classification model 76 00:03:32,066 --> 00:03:34,800 which will be the decision tree regression model. 77 00:03:34,800 --> 00:03:37,600 I look forward to seeing you in its practical activity. 78 00:03:37,600 --> 00:03:39,666 And until then enjoy machine learning.