why is my svm taking much time to run what changes should i make in my code?

Question

from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

reg_svr = SVR(kernel='rbf')
reg_svr.fit(X_train_scaled, y_train)

test['prediction_svr'] = reg_svr.predict(X_test_scaled)

score_svr = np.sqrt(mean_squared_error(test['PJME_MW'], test['prediction_svr']))
print(f'RMSE Score on Test set (SVR): {score_svr:0.2f}')

Answer 1

There are several possibilities to speed up your SVM training. Follow the steps mentioned in the answer

• SVC classifier taking too much time for training

and

• SVM using scikit learn runs endlessly and never completes execution