Due to recent updates, all users are required to create an Altair One account to login to the RapidMiner community. Click the Register button to create your account using the same email that you have previously used to login to the RapidMiner community. This will ensure that any previously created content will be synced to your Altair One account. Once you login, you will be asked to provide a username that identifies you to other Community users. Email us at Community with questions.
hyperplane equation
Contributor II
I am trying to solve a simple toy classification problem ( a linear one) using the "linear regression", perceptron and svm (libsvm). Then read the parameters and try to find out the hyperplane equation
and I got confused with the information that I got in the "text view" where the models are written. The bias term (b) of the model do not seem to be always written using same notation
I am trying to interpret as w1*attr1+w2*attr2+ ...+b.
In a 2D problem the hyperplane equation should then come from w1*attr1+w2*attr3+b=0
The problem I was solving was
((attr1,attr2), label) -----> ( (-1,1),1); ((1,-1),1),((1,1),1),(-1,-1),-1).
with linear regression I got the following equation 0.25*attr1+0.25*attr2+0.25
with perceptron I got : intercept : -0.25 w(attr1)=0.25, w(attr2)=0.25
with svm I got : bias (offset): -1.0 w(attr1)=0.5 w(attr2)=0.5.
The question is the “bias” or “intercept” are according with the output of linear regression. And in particular in these case the solution found seems not solve the problem. What I am doing wrong?
AMT
and I got confused with the information that I got in the "text view" where the models are written. The bias term (b) of the model do not seem to be always written using same notation
I am trying to interpret as w1*attr1+w2*attr2+ ...+b.
In a 2D problem the hyperplane equation should then come from w1*attr1+w2*attr3+b=0
The problem I was solving was
((attr1,attr2), label) -----> ( (-1,1),1); ((1,-1),1),((1,1),1),(-1,-1),-1).
with linear regression I got the following equation 0.25*attr1+0.25*attr2+0.25
with perceptron I got : intercept : -0.25 w(attr1)=0.25, w(attr2)=0.25
with svm I got : bias (offset): -1.0 w(attr1)=0.5 w(attr2)=0.5.
The question is the “bias” or “intercept” are according with the output of linear regression. And in particular in these case the solution found seems not solve the problem. What I am doing wrong?
AMT
0
Answers
I think you need to use more data points.
There are many more functions that can fit these 4 data points perfectly,
other then the function f(x,y) = positive(x) or positive(y).
Also, it is important use use labels like a, b, instead of 1, -1, else its not a classification problem, but a regression problem.
You seem to want to find a function that is something like
y(x) = x - 1.
and then f(x,y) = y(x) > 0?