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2. difference between LinearRegression and svm.SVR (kernel="linear")

   stackoverflow.com/questions/46970888

   svm.SVR: The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. First of all, because output is a real number it becomes very difficult to predict the information at hand, which has infinite possibilities.

3. Support Vector Regression multiple outputs - Stack Overflow

   stackoverflow.com/questions/53703431

   8. I am currently testing Support Vector Regression (SVR) for a regression problem with two outputs. This means that Y_train_data has two values for each sample. Since SVR can only produce a single output, I use the MultiOutputRegressor from scikit. svr_reg = MultiOutputRegressor(SVR(kernel=_kernel, C=_C, gamma=_gamma, degree=_degree, coef0 ...

4. Multi-target regression using scikit-learn - Stack Overflow

   stackoverflow.com/questions/57704609

   Another alternative to the random forest approach would be to use an adapted version of Support Vector Regression, that fits multi-target regression problems. The advantage over fitting SVR with MultiOutputRegressor is that this method takes the underlying correlations between the multiple targets into account and hence should perform better.

5. How do you forecast future values using support vector regression...

   stackoverflow.com/questions/64198200

   I am trying to forecast for future values of a periodic position dependent on time (x ~ time), univariate forecasting using support vector regression. The model fits well on train data but then trails into a straight line when evaluated on test data.

6. Finding regression equation from support vector regression

   stackoverflow.com/questions/57062387

   I'm currently using Python's scikit-learn to create a support vector regression model, and I was wondering how one would go about finding the explicit regression equation of our target variable in terms of our predictors. It doesn't have to be simple or pretty, but is there a method Python has to output this (for a polynomial kernel, specifically)?

7. Predictions using Support Vector Regression - Stack Overflow

   stackoverflow.com/questions/51350585

   Please do consider scaling your data before performing the regression. import numpy as np from sklearn import svm import matplotlib.pyplot as plt n_samples, n_features = 10, 4 # your four features a,b,c,d are the n_features np.random.seed(0) y_e = np.random.randn(n_samples) y_f = np.random.randn(n_samples) # your input array should be formatted ...

8. Time series forecasting with support vector regression

   stackoverflow.com/questions/24517858

   I'm trying to perform a simple time series prediction using support vector regression. I am trying to understand the answer provided here. I adapted Tom's code to reflect the answer provided: x.append(Y[a:b]) a += 1. b += 1. y.append(Y[b]) b += 1. However, I still get the same behavior -- the prediction just returns the value from the last ...

9. python - Feature Importance with SVR - Stack Overflow

   stackoverflow.com/questions/70467781

   Permutation feature selection can be used via the permutation_importance () function that takes a fit model, a dataset (train or test dataset is fine), and a scoring function. model = SVR() # fit the model. model.fit(X, y) # perform permutation importance. results = permutation_importance(model, X, y, scoring='neg_mean_squared_error')

10. How to plot support vectors for support vector regression?

    stackoverflow.com/questions/60734716

    I am trying to solve hard margin support vector regression and plot hyperplane and support vectors for a dataset. As you know, hard margin is solved with the below assumption: I solved the problem but when I want to plot decision boundaries and support vectors, I face the below problem.

11. Finding pvalue, r and adjusted r^2 for SVR regression model

    stackoverflow.com/questions/52391030

    p_value = 1-stats.chi2.cdf(x = chi_squared_value, df = 32) df is the degrees of freedom, the critical value is your value to surpass / underpass for your alternate hypothesis rejection / acceptation. The r^2 value can be calculated using r2 score. For example: from sklearn.metrics import r2_score. r2_score(y, y_hat)