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These are the user uploaded subtitles that are being translated: 1 00:00:00,740 --> 00:00:03,120 The choice of features can have 2 00:00:03,120 --> 00:00:06,090 a huge impact on your learning algorithm's performance. 3 00:00:06,090 --> 00:00:08,700 In fact, for many practical applications, 4 00:00:08,700 --> 00:00:11,130 choosing or entering the right features is 5 00:00:11,130 --> 00:00:14,085 a critical step to making the algorithm work well. 6 00:00:14,085 --> 00:00:17,340 In this video, let's take a look at how you can choose or 7 00:00:17,340 --> 00:00:19,365 engineer the most appropriate features 8 00:00:19,365 --> 00:00:21,150 for your learning algorithm. 9 00:00:21,150 --> 00:00:24,300 Let's take a look at feature engineering by revisiting 10 00:00:24,300 --> 00:00:27,840 the example of predicting the price of a house. 11 00:00:27,840 --> 00:00:31,140 Say you have two features for each house. 12 00:00:31,140 --> 00:00:33,975 X_1 is the width of the lot size 13 00:00:33,975 --> 00:00:37,200 of the plots of land that the house is built on. 14 00:00:37,200 --> 00:00:39,345 This in real state is also 15 00:00:39,345 --> 00:00:42,000 called the frontage of the lot, 16 00:00:42,000 --> 00:00:44,070 and the second feature, 17 00:00:44,070 --> 00:00:47,465 x_2, is the depth of the lot size of, 18 00:00:47,465 --> 00:00:49,340 lets assume the rectangular plot 19 00:00:49,340 --> 00:00:51,590 of land that the house was built on. 20 00:00:51,590 --> 00:00:54,430 Given these two features, x_1 and x_2, 21 00:00:54,430 --> 00:00:57,755 you might build a model like this where f of x 22 00:00:57,755 --> 00:01:02,110 is w_1x_1 plus w_2x_2 plus b, 23 00:01:02,110 --> 00:01:04,875 where x_1 is the frontage or width, 24 00:01:04,875 --> 00:01:07,680 and x_2 is the depth. 25 00:01:07,680 --> 00:01:10,045 This model might work okay. 26 00:01:10,045 --> 00:01:12,020 But here's another option for how 27 00:01:12,020 --> 00:01:13,700 you might choose a different way to 28 00:01:13,700 --> 00:01:15,335 use these features in the model 29 00:01:15,335 --> 00:01:17,160 that could be even more effective. 30 00:01:17,160 --> 00:01:18,830 You might notice that the area of 31 00:01:18,830 --> 00:01:20,540 the land can be calculated 32 00:01:20,540 --> 00:01:24,375 as the frontage or width times the depth. 33 00:01:24,375 --> 00:01:27,020 You may have an intuition that 34 00:01:27,020 --> 00:01:30,200 the area of the land is more predictive of the price, 35 00:01:30,200 --> 00:01:33,940 than the frontage and depth as separate features. 36 00:01:33,940 --> 00:01:36,570 You might define a new feature, 37 00:01:36,570 --> 00:01:39,660 x_3, as x_1 times x_2. 38 00:01:39,660 --> 00:01:41,795 This new feature x_3 is 39 00:01:41,795 --> 00:01:44,875 equal to the area of the plot of land. 40 00:01:44,875 --> 00:01:48,785 With this feature, you can then have a model f_w, 41 00:01:48,785 --> 00:01:53,255 b of x equals w_1x_1 plus w_2x_2 42 00:01:53,255 --> 00:01:56,110 plus w_3x_3 plus b 43 00:01:56,110 --> 00:01:59,330 so that the model can now choose parameters w_1, 44 00:01:59,330 --> 00:02:01,205 w_2, and w_3, 45 00:02:01,205 --> 00:02:03,440 depending on whether the data shows that 46 00:02:03,440 --> 00:02:06,335 the frontage or the depth or the area 47 00:02:06,335 --> 00:02:08,750 x_3 of the lot turns out to be 48 00:02:08,750 --> 00:02:10,250 the most important thing for 49 00:02:10,250 --> 00:02:12,430 predicting the price of the house. 50 00:02:12,430 --> 00:02:15,469 What we just did, creating a new feature 51 00:02:15,469 --> 00:02:19,180 is an example of what's called feature engineering, 52 00:02:19,180 --> 00:02:21,380 in which you might use your knowledge or 53 00:02:21,380 --> 00:02:24,460 intuition about the problem to design new features 54 00:02:24,460 --> 00:02:26,270 usually by transforming or 55 00:02:26,270 --> 00:02:28,010 combining the original features of 56 00:02:28,010 --> 00:02:29,810 the problem in order to make it 57 00:02:29,810 --> 00:02:31,670 easier for the learning algorithm 58 00:02:31,670 --> 00:02:33,545 to make accurate predictions. 59 00:02:33,545 --> 00:02:35,420 Depending on what insights you 60 00:02:35,420 --> 00:02:37,204 may have into the application, 61 00:02:37,204 --> 00:02:38,600 rather than just taking 62 00:02:38,600 --> 00:02:40,190 the features that you happen to have 63 00:02:40,190 --> 00:02:43,745 started off with sometimes by defining new features, 64 00:02:43,745 --> 00:02:47,500 you might be able to get a much better model. 65 00:02:47,500 --> 00:02:50,625 That's feature engineering. 66 00:02:50,625 --> 00:02:54,260 It turns out that this one flavor of feature engineering, 67 00:02:54,260 --> 00:02:56,780 that allow you to fit not just straight lines, 68 00:02:56,780 --> 00:03:00,095 but curves, non-linear functions to your data. 69 00:03:00,095 --> 00:03:02,000 Let's take a look in the next video 70 00:03:02,000 --> 00:03:04,170 at how you can do that.4976

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