1 00:00:01,740 --> 00:00:08,370 Now, in this video, we are going to bring the model with a radial cannon as we go and indeed your 2 00:00:08,390 --> 00:00:12,540 elected radial camel as to hyper parameters. 3 00:00:13,440 --> 00:00:14,460 One is the gamma. 4 00:00:15,680 --> 00:00:22,860 Which 60 days later, importance of the distance between the retail company and other point. 5 00:00:24,500 --> 00:00:26,480 And the second hyper barometer is the cost. 6 00:00:27,730 --> 00:00:30,320 This we have been discussing since Línea comment. 7 00:00:32,480 --> 00:00:36,770 So fitting that a deal cardinal is the same. 8 00:00:37,370 --> 00:00:43,970 It uses the SBM function only that the cardinal value will be radial here instead of linear or binomial. 9 00:00:45,830 --> 00:00:47,910 We will store this in SVM fit. 10 00:00:48,230 --> 00:00:49,970 Ah, it will be for radio. 11 00:00:51,480 --> 00:00:52,480 And if I run this. 12 00:00:52,490 --> 00:01:01,310 Come on, you can see that I have a similar SBM print on variable which has the information of radial 13 00:01:01,310 --> 00:01:02,960 gunnin as a model. 14 00:01:04,340 --> 00:01:05,480 Now you can use this. 15 00:01:06,890 --> 00:01:12,000 Variable to predict the values on the best set and take out its performance. 16 00:01:13,020 --> 00:01:19,260 But it does not really make sense because we need to find out that value of these hyper barometers at 17 00:01:19,260 --> 00:01:22,080 which we expect that the test performance will be better. 18 00:01:22,950 --> 00:01:27,360 So we want to find out the value of glamour and cost. 19 00:01:28,260 --> 00:01:32,490 Or that we will use the function and we will give it these values of cost. 20 00:01:33,270 --> 00:01:35,970 You can see that I have provided logarithmic range. 21 00:01:36,340 --> 00:01:42,750 It start from zero point zero zero one, then zero point zero one and goes up to 1000. 22 00:01:44,110 --> 00:01:53,800 And these values of Gama let from Datapoint to one to 50, I'll reduce some of these, I'll remove this 23 00:01:54,700 --> 00:02:00,470 to this forward so that it does not take too much time while running. 24 00:02:01,090 --> 00:02:04,150 And I've also added this glossy article to Ford. 25 00:02:05,320 --> 00:02:09,480 Which means that there should be full cross validation sets instead of thing. 26 00:02:10,660 --> 00:02:12,010 So everything is the same. 27 00:02:12,150 --> 00:02:13,540 Just got radial. 28 00:02:14,350 --> 00:02:21,250 And in the hybrid barometer's, instead of the degree we have Gamma Gloucester's the same as earlier 29 00:02:22,150 --> 00:02:24,130 and we have a cross-validation take out for. 30 00:02:25,710 --> 00:02:26,680 So I Londis, come on. 31 00:02:31,410 --> 00:02:37,440 And we have this tuned out out of what evil created, this video will condense the information of all 32 00:02:37,440 --> 00:02:43,590 the combination of models that could be created using these values of cost and gamma. 33 00:02:48,800 --> 00:02:54,020 If you want to look at the information in this, you're not out. 34 00:02:54,170 --> 00:02:54,470 Out. 35 00:02:55,190 --> 00:02:55,690 You can go. 36 00:02:55,790 --> 00:03:02,300 Somebody come on on this and you can see that it contains the information of all the different combinations. 37 00:03:02,510 --> 00:03:08,230 So for cost of this, Garmo, of this, we get a cross ventilation at add value. 38 00:03:08,300 --> 00:03:08,720 This. 39 00:03:09,870 --> 00:03:10,410 And so on. 40 00:03:10,470 --> 00:03:19,860 So all the different models that ran across cross-validation error values did this best model variable 41 00:03:19,980 --> 00:03:28,300 of do not old are contains that particular combination of cost and darma, which has the minimum crossway 42 00:03:28,300 --> 00:03:28,980 religion in it. 43 00:03:30,090 --> 00:03:34,830 So I run this command to store the information of that best model. 44 00:03:34,940 --> 00:03:36,250 Invest more are. 45 00:03:38,660 --> 00:03:47,060 Now, to look at this best mark up and underscore man, and you can see that we ran a radial SVM. 46 00:03:48,760 --> 00:03:56,110 The cost at which we got minimum cross-validation error is Cees equal to 10. 47 00:03:57,460 --> 00:04:04,450 The number of support vectors are coming out to be and a 19 out of three eight one sixty two of them 48 00:04:04,450 --> 00:04:06,940 are on one side, 157 out on the other. 49 00:04:09,350 --> 00:04:13,050 And we have a total accuracy of nearly sixty one point eight. 50 00:04:13,530 --> 00:04:15,450 It's a four fold cross-validation. 51 00:04:17,190 --> 00:04:18,630 Now using this model. 52 00:04:20,300 --> 00:04:27,620 We are going to predict the values on the deficit and those values would be stored in wiped out. 53 00:04:29,740 --> 00:04:36,310 And now we are going to compare how our predicted values fared against the actual values in the essay. 54 00:04:36,940 --> 00:04:37,870 So we'll run this. 55 00:04:38,210 --> 00:04:38,750 It will. 56 00:04:41,390 --> 00:04:48,350 And you can see that we are correctly predicting these 29 values for which we predict that Oscar will 57 00:04:48,350 --> 00:04:50,240 not be given to the movie. 58 00:04:50,570 --> 00:04:53,210 And actually the movie did not get the Oscar. 59 00:04:53,980 --> 00:04:59,450 And for at least 37 movies for which we predicted that it will get an Oscar. 60 00:04:59,690 --> 00:05:00,890 And actually, it the. 61 00:05:02,310 --> 00:05:08,930 So we get an accuracy of 66 out of when they do it in percentage terms. 62 00:05:08,970 --> 00:05:12,570 That is 66 divided by one, they do it. 63 00:05:14,170 --> 00:05:16,320 It is nearly sixty one point one percent. 64 00:05:20,350 --> 00:05:26,890 So you can see that when we then are polynomial Cottonelle also, it was suggesting that there is more 65 00:05:26,890 --> 00:05:31,720 of a linear relationship between the predictive variables and the response would even. 66 00:05:33,630 --> 00:05:41,250 And when we tried to feed a canal, cardinal swim on variables, we tell more of a linear relationship. 67 00:05:41,940 --> 00:05:45,680 It does not perform better than the polynomial order linear cardinal. 68 00:05:47,280 --> 00:05:51,130 So using the right gomel is very important. 69 00:05:51,780 --> 00:05:55,080 If that is the new relationship, a linear cardinal will do better. 70 00:05:55,620 --> 00:05:59,990 If there is a non-linear relationship, really a lot more normal cardinals would do better. 71 00:06:02,550 --> 00:06:04,450 And this was all about classification. 72 00:06:04,960 --> 00:06:12,070 That is that the response variable of the variable that we were trying to predict had glasses and we 73 00:06:12,070 --> 00:06:17,890 were assigning glasses using the created model on a test set or a music. 74 00:06:19,710 --> 00:06:24,090 In the next video, we will learn how to do regression using SBM. 75 00:06:24,430 --> 00:06:29,950 That is how to predict continuous values instead of classes using the SBM technique.