# How to predict responses of new data from linear SVM model?

41 views (last 30 days)
minhyuk jeung on 21 May 2024 at 8:24
Commented: minhyuk jeung on 27 May 2024 at 1:13
Hello, I am trying to calculate new predictions with trained linear SVM regression (4 input variables and 1 output).
from this equation, I tried:
x_new = [8.9 10.2 42.8 44.8]; % New input variables (4 variables)
supportVectors = Mdl.SupportVectors;
alpha = Mdl.Alpha;
bias = Mdl.Bias;
w = (alpha .* supportVectors)';
prediction = (w .* x_new) + bias;
-------------------------------------------------------------------
However, when I compare the results of the code below, there's a different value:
prediction = Mdl.predict(x_new);
If there is a code that calculates the output value when a new observation comes in through the trained SVM, please help me.

Angelo Yeo on 21 May 2024 at 11:26
Edited: Angelo Yeo on 27 May 2024 at 0:14
Here is the example I cooked up for you. I assumed that you would want to use a linear kernel. For more information on manual prediction, see the doc below.
%% Setup
% I will generate random data for two groups.
X = randn(100, 1) * 1 + 9; % It's just my random guess.
X = [X; randn(100, 1) * 1 + 43]; % It's just my random guess.
Y = [ones(100,1)*0; ones(100,1)*1];
Mdl = fitcsvm(X, Y, "KernelFunction", "linear", "Standardize", true);
%% prediction test
x_new = [8.9 10.2 42.8 44.8]; % New input variables (4 variables)
% We need to normalize the new input for manual prediction
x_new_norm = (x_new - Mdl.Mu) / Mdl.Sigma;
supportVectors = Mdl.SupportVectors;
alpha = Mdl.Alpha;
bias = Mdl.Bias;
% w = (alpha .* supportVectors)';
s = Mdl.KernelParameters.Scale;
beta = Mdl.Beta;
prediction = (x_new_norm/s)'*beta + bias;
prediction_label = prediction > 0
prediction_label = 4x1 logical array
0 0 1 1
Angelo Yeo on 27 May 2024 at 0:18
안녕하세요. 아래와 같이 진행해보시면 좋을 것 같습니다.
inputdata = readmatrix('inputdata.xlsx', Sheet = "Sheet1");
outputdata = readmatrix('outputdata.xlsx', Sheet = "Sheet1");
predictor = inputdata(1:100,1:4); % 4 input variable
target_variable = outputdata(1:100,1); % 1 output variable
X = predictor;
Y = target_variable;
Mdl = fitrsvm(X,Y,"Standardize",true);
pred_val = predict(Mdl, X);
%% For the new prediction
x_new = [8.9 10.2 42.8 44.8]; % New input variables (4 variables)
s = Mdl.KernelParameters.Scale;
beta = Mdl.Beta;
bias = Mdl.Bias;
x_new_norm = (x_new - Mdl.Mu) ./ Mdl.Sigma ;
x_new_norm/s * beta + bias % compare this and the predict value below
ans = 0.0714
Mdl.predict(x_new)
ans = 0.0714
SVR에 대한 자세한 설명은 아래 문서에서도 확인하실 수 있습니다.
minhyuk jeung on 27 May 2024 at 1:13
위 코드로 실행해보니 해결되었습니다.
감사합니다!! ㅠ

### Categories

Find more on Resampling Techniques in Help Center and File Exchange

R2024a

### Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!