1 00:00:00,780 --> 00:00:07,050 Before we start discussing declassification techniques, we must take a moment to answer the question, 2 00:00:07,500 --> 00:00:10,800 why can't we use linear regression to do classification? 3 00:00:12,090 --> 00:00:13,200 Consider this example. 4 00:00:15,270 --> 00:00:22,500 I have the data set of some profile of people which include whether that's true or not, that great 5 00:00:22,500 --> 00:00:28,090 card balance and their incomes corresponding to each profile. 6 00:00:28,530 --> 00:00:30,390 I have the great risk profile. 7 00:00:30,780 --> 00:00:34,980 That is whether they defaulted on their loan payment or not. 8 00:00:35,490 --> 00:00:38,490 That is whether they repaid their loan or not. 9 00:00:39,690 --> 00:00:42,800 So if they repay, the default value will be no. 10 00:00:43,050 --> 00:00:44,880 If they do not, they are defaulting. 11 00:00:45,330 --> 00:00:47,410 That is their default value will be, yes. 12 00:00:49,370 --> 00:00:54,360 So using this data, I want to classify how risky a particular profiler's. 13 00:00:57,210 --> 00:01:05,010 Now, if I transform this data so that hiders is represented by one that is wherever default values, 14 00:01:05,010 --> 00:01:08,570 yes, I put one and wherever there is lowest. 15 00:01:08,990 --> 00:01:10,620 I represented by minus one. 16 00:01:11,340 --> 00:01:12,990 I can run a linear regression on it. 17 00:01:16,140 --> 00:01:21,850 So with this coding, when I an integration aggression, it will give me some predicted continuous values 18 00:01:21,850 --> 00:01:25,540 of the response variable, and I can say that. 19 00:01:26,800 --> 00:01:32,200 If the predicted value of the response variable is greater than zero, then it belongs to the class 20 00:01:32,200 --> 00:01:34,220 Mondelēz one, which is hiders. 21 00:01:35,300 --> 00:01:41,390 And if it is less than zero, it belongs to the class Magda's minus one, which is less risk. 22 00:01:42,860 --> 00:01:46,880 So this is a fairly good classifying technique, but it has several problems. 23 00:01:47,720 --> 00:01:53,690 The first problem we face with this classifier, which is using linear regression, is if we have more 24 00:01:53,690 --> 00:01:55,950 than two levels in the response variable. 25 00:01:56,600 --> 00:02:00,440 For example, in the response variable instead of yes and no. 26 00:02:00,560 --> 00:02:04,220 If I had credit risk, low, medium and high. 27 00:02:05,270 --> 00:02:07,970 Such a scenario cannot be handled using this. 28 00:02:07,970 --> 00:02:08,840 Classify it. 29 00:02:10,290 --> 00:02:17,640 So as we discussed in dummy variable Clie creation, Lichter, we cannot simply assign zero one two 30 00:02:17,760 --> 00:02:19,380 values to these three classes. 31 00:02:21,000 --> 00:02:23,610 We have to cleared and minus one dummy variables. 32 00:02:24,630 --> 00:02:30,150 In this case, we will have to create three minus one, that is to do me with Abels for credit risk. 33 00:02:32,640 --> 00:02:34,220 Then we will have to response. 34 00:02:34,230 --> 00:02:37,870 We're able to be predicted, which cannot be handled. 35 00:02:38,520 --> 00:02:44,580 Therefore, if response variable has more than two levels, linear regression cannot be used. 36 00:02:47,280 --> 00:02:54,360 Second problem is that the predicted value of life from this classify, it can not be considered as 37 00:02:54,360 --> 00:02:57,380 the probability of Y belonging to a particular class. 38 00:02:59,320 --> 00:03:06,280 Since its values ranging from minus infinity to plus infinity, as you can see by this graph, it's 39 00:03:06,280 --> 00:03:07,030 a straight line. 40 00:03:07,450 --> 00:03:10,760 If you extend it further, it will go up to plus infinity. 41 00:03:11,320 --> 00:03:14,680 And if you extend it backwards, it can go to minus infinity. 42 00:03:16,180 --> 00:03:19,600 But my probability values can only range between zero and one. 43 00:03:20,530 --> 00:03:27,820 So even though it is classifying in two classes, we are not sure about how confident we can be with 44 00:03:27,820 --> 00:03:28,940 deep predictive response. 45 00:03:31,180 --> 00:03:35,590 The third problem we face is the impact of outlaying values. 46 00:03:36,250 --> 00:03:40,100 I'm not talking about the outliers which we handle in Prepossessing. 47 00:03:40,660 --> 00:03:44,470 I'm talking about genuine but large or very small values. 48 00:03:46,830 --> 00:03:51,780 To fit these far away points, regression model will change this slope. 49 00:03:52,320 --> 00:03:56,220 So imagine if I have another point over here. 50 00:03:57,000 --> 00:04:00,810 This line will try to change its slope to accommodate this point. 51 00:04:01,380 --> 00:04:03,570 And this line will move something like this. 52 00:04:05,220 --> 00:04:09,180 And in this process, we will wrongly classify a lot of points. 53 00:04:11,070 --> 00:04:14,470 Does a Linnean model is very sensitive to outline point. 54 00:04:14,880 --> 00:04:18,810 And when outlaying points out present, it gives out wrong predictions. 55 00:04:20,710 --> 00:04:23,790 In the next we do I will tell you about logistic regression. 56 00:04:24,420 --> 00:04:29,250 I will show you these problems of linear regression graphically and show you how logistic regression 57 00:04:29,460 --> 00:04:30,150 overcomes them.