1
00:00:00,300 --> 00:00:02,700
Hello
my friends, welcome to this new tutorial.

2
00:00:02,700 --> 00:00:06,933
And now we're about to train the SVR model
on the whole data set.

3
00:00:07,200 --> 00:00:10,300
After a successful data
preprocessing phase

4
00:00:10,300 --> 00:00:14,066
with your new experience
in feature scaling,

5
00:00:14,066 --> 00:00:17,700
because now you can handle very well
all the different situations.

6
00:00:18,000 --> 00:00:18,366
All right.

7
00:00:18,366 --> 00:00:22,000
So let's train
that SVR model on the whole data set.

8
00:00:22,000 --> 00:00:22,400
Of course,

9
00:00:22,400 --> 00:00:26,933
because we did not do a split of the data
set between training and test sets.

10
00:00:27,166 --> 00:00:28,000
So there you go.

11
00:00:28,000 --> 00:00:30,066
Let's create a new code cell.

12
00:00:30,066 --> 00:00:35,366
And now let's build of course
in the most efficient way the SVR model.

13
00:00:35,366 --> 00:00:37,266
So we're going to build it
with scikit learn.

14
00:00:37,266 --> 00:00:41,266
Of course I remind that scikit
learn is the best data science library

15
00:00:41,266 --> 00:00:45,000
excluding deep learning, because of course
we have TensorFlow and PyTorch.

16
00:00:45,000 --> 00:00:48,900
But for any machine learning model
that is not based on neural networks,

17
00:00:49,066 --> 00:00:53,133
well, scikit learn is definitely by far
the best data science library.

18
00:00:53,133 --> 00:00:56,600
And for what we are interested
in right now, you know the SVR model.

19
00:00:56,700 --> 00:01:01,766
Well, we will build it with a class
called SVR, as simple as that,

20
00:01:01,933 --> 00:01:05,633
which belongs to a module of scikit
learn called SVM.

21
00:01:05,833 --> 00:01:08,700
And so we have to start here from

22
00:01:08,700 --> 00:01:13,066
first scikit learn to access the scikit
learn library from which

23
00:01:13,233 --> 00:01:17,400
we're going to get access to that
SVM modules.

24
00:01:17,400 --> 00:01:23,500
That's the module from which
we're going to import that SVR class.

25
00:01:23,500 --> 00:01:24,500
Perfect.

26
00:01:24,500 --> 00:01:25,833
Now we have the class.

27
00:01:25,833 --> 00:01:28,833
And you of course
know the next natural step.

28
00:01:28,833 --> 00:01:31,833
Any time you import a class,
next natural step is, of course,

29
00:01:32,000 --> 00:01:36,066
to create an object
or an instance of this class.

30
00:01:36,266 --> 00:01:39,000
And we're going to call that object
regressor.

31
00:01:39,000 --> 00:01:40,233
Just like before.

32
00:01:40,233 --> 00:01:44,400
Because this instance or object of the SVR
class

33
00:01:44,400 --> 00:01:48,600
is nothing else than the SVR regressor
itself.

34
00:01:48,600 --> 00:01:51,900
You know, the support vector
for regression regressor.

35
00:01:52,266 --> 00:01:53,200
So regressor.

36
00:01:53,200 --> 00:01:56,633
And then of course
we are going to call this class SVR.

37
00:01:56,800 --> 00:01:58,066
Add some parenthesis.

38
00:01:58,066 --> 00:02:01,400
And this time we have to input a parameter

39
00:02:01,833 --> 00:02:06,066
because indeed remember in the intuition
lecture of support vector regression.

40
00:02:06,066 --> 00:02:09,766
And you will also see in the intuition
lecture of the support

41
00:02:09,766 --> 00:02:12,766
vector machine in the next part, part
three classification.

42
00:02:12,933 --> 00:02:15,633
Well,
you actually have what we call kernels

43
00:02:15,633 --> 00:02:19,900
which can either learn some linear
relationships and that's the linear

44
00:02:19,900 --> 00:02:23,433
kernel or non-linear
relationships in your data set,

45
00:02:23,666 --> 00:02:27,966
which are the nonlinear kernels
such as the RBF radial basis.

46
00:02:27,966 --> 00:02:30,366
I will actually show it to you right away.

47
00:02:30,366 --> 00:02:35,300
You know, that's the Gaussian RBF kernel
which is given by this formula.

48
00:02:35,300 --> 00:02:38,300
And I can actually show you
the plot of that kernel.

49
00:02:38,300 --> 00:02:39,133
Here it is.

50
00:02:39,133 --> 00:02:41,366
So that's the Gaussian RBF kernel.

51
00:02:41,366 --> 00:02:44,500
And then you also have some other kernels
which I've prepared

52
00:02:44,500 --> 00:02:48,333
here on this great website,
showing clearly the different kernels

53
00:02:48,333 --> 00:02:52,333
of the support vector machine,
but also the SVR, because as we are,

54
00:02:52,333 --> 00:02:56,366
is nothing else than a support vector
machine model for regression.

55
00:02:56,700 --> 00:02:58,033
So let's see here they are.

56
00:02:58,033 --> 00:03:01,466
There is the polynomial kernel
adapted for a non-linear data set.

57
00:03:01,566 --> 00:03:04,800
You have two Gaussian kernel
which has a classic Gaussian function.

58
00:03:05,033 --> 00:03:06,900
Then the Gaussian radial basis function.

59
00:03:06,900 --> 00:03:08,833
The most widely used one.

60
00:03:08,833 --> 00:03:11,000
And that's actually the one we will use.

61
00:03:11,000 --> 00:03:14,266
Then you have the Laplace one,
the hyperbolic tangent kernel as well.

62
00:03:14,266 --> 00:03:15,933
That's a popular one sigma one.

63
00:03:15,933 --> 00:03:18,300
Well you have all of them here.

64
00:03:18,300 --> 00:03:21,000
And so if you're curious yes,
you can have a look at them.

65
00:03:21,000 --> 00:03:25,500
But the one we will use for
our implementation will be

66
00:03:25,500 --> 00:03:29,500
and that's the one I recommend each time
you experiment with an SVR model.

67
00:03:29,633 --> 00:03:33,466
And that's the radial basis function
kernel, the RBF kernel.

68
00:03:33,733 --> 00:03:36,933
And that's exactly what we have
to input here in our parameters.

69
00:03:37,200 --> 00:03:39,700
And so the name of that
parameter is kernel.

70
00:03:39,700 --> 00:03:41,400
So that's the name of the parameter.

71
00:03:41,400 --> 00:03:44,400
And then the value we want
for that parameter which you know

72
00:03:44,433 --> 00:03:49,933
corresponds to the radial basis function
has the code name in quotes are the f.

73
00:03:50,133 --> 00:03:51,000
And that's it.

74
00:03:51,000 --> 00:03:53,766
That's only what we have to input here.

75
00:03:53,766 --> 00:03:58,866
So that basically creates the SVR model
with the radial basis function kernel.

76
00:03:59,100 --> 00:04:00,866
And so now that means only one thing.

77
00:04:00,866 --> 00:04:03,833
That means
that we already have built the model.

78
00:04:03,833 --> 00:04:06,000
We already have the SVR model itself.

79
00:04:06,000 --> 00:04:09,133
And so now the last final natural step
is to of course

80
00:04:09,133 --> 00:04:13,000
train that regressor on the
whole data set.

81
00:04:13,000 --> 00:04:15,100
Right.
Which is also the training set. Okay.

82
00:04:15,100 --> 00:04:18,000
So let's do this.
You know exactly how to do this.

83
00:04:18,000 --> 00:04:20,700
You know from this point
it's exactly the same as before.

84
00:04:20,700 --> 00:04:24,366
You know, the same function,
which is of course the fit function

85
00:04:24,633 --> 00:04:29,633
and which will train this SVR model
on your whole data set,

86
00:04:29,633 --> 00:04:33,733
right, to learn the correlations between
the position levels and the salaries.

87
00:04:33,966 --> 00:04:36,966
And all this done with the radial basis
function kernel.

88
00:04:37,000 --> 00:04:37,333
All right.

89
00:04:37,333 --> 00:04:40,266
So let's do this. Let's train
our regressor.

90
00:04:40,266 --> 00:04:42,300
So we take our regressor object first.

91
00:04:42,300 --> 00:04:44,633
And as usual we add here a dot.

92
00:04:44,633 --> 00:04:49,666
And then the fit method
which takes as input this time not xtrain

93
00:04:49,666 --> 00:04:53,100
and y train because we have not created
a separate training set.

94
00:04:53,266 --> 00:04:57,933
But of course this time the whole data set
which remember was feature scaled,

95
00:04:57,933 --> 00:05:03,233
you know, both on the matrix of features
export and the dependent variable y part.

96
00:05:03,500 --> 00:05:08,400
And so that's exactly what we have
to input here both x and y.

97
00:05:08,600 --> 00:05:10,733
And that's it. Congratulations.

98
00:05:10,733 --> 00:05:14,400
Now you know how to build and train
and SVR model.

99
00:05:14,400 --> 00:05:19,333
After a successful data preprocessing
phase including special feature scaling.

100
00:05:20,000 --> 00:05:20,566
All right.

101
00:05:20,566 --> 00:05:24,300
And so now we have a very interesting
next step.

102
00:05:24,566 --> 00:05:27,466
It is the step
where we predict the new result.

103
00:05:27,466 --> 00:05:30,466
And this next step is very interesting
because it will teach you

104
00:05:30,500 --> 00:05:34,533
how to reverse
the scaling of your prediction.

105
00:05:34,533 --> 00:05:38,800
Because you will see that when we apply
the predict method to predict

106
00:05:38,800 --> 00:05:43,800
this new result, well, it will be returned
in the scale that was used for Y.

107
00:05:43,800 --> 00:05:44,266
Right?

108
00:05:44,266 --> 00:05:47,100
The new scale of Y
after it was transformed.

109
00:05:47,100 --> 00:05:49,600
And so we will have to reverse this
transformation.

110
00:05:49,600 --> 00:05:53,900
We'll have to reverse the scaling
in order to get the original scale of Y.

111
00:05:54,033 --> 00:05:57,200
And now we'll teach you
exactly how to do it in the next tutorial.