svm

问题 I did a classification with svm using e1071 . The goal is to predict type through all other variables in dtm . dtm[140:145] %>% str() 'data.frame': 385 obs. of 6 variables: $ think : num 0 0 0 0 0 0 0 0 0 0 ... $ actually: num 0 0 0 0 0 0 0 0 0 0 ... $ comes : num 0 0 0 0 0 0 0 0 0 0 ... $ able : num 0 0 0 0 0 0 0 0 0 0 ... $ hours : num 0 0 0 0 0 0 0 0 0 0 ... $ type : Factor w/ 4 levels "-1","0","1","9": 4 3 3 3 4 1 4 4 4 3 ... To train/test the model, I used the 10-fold-cross-validation.

问题 I have multi class labels and want to compute the accuracy of my model. I am kind of confused on which sklearn function I need to use. As far as I understood the below code is only used for the binary classification. # dividing X, y into train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,random_state = 0) # training a linear SVM classifier from sklearn.svm import SVC svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) svm

问题 I found sklearn.svm.LinearSVC and sklearn.svm.SVC(kernel='linear') and they seem very similar to me, but I get very different results on Reuters. sklearn.svm.LinearSVC: 81.05% in 28.87s train / 9.71s test sklearn.svm.SVC : 33.55% in 6536.53s train / 2418.62s test Both have a linear kernel. The tolerance of the LinearSVC is higher than the one of SVC: LinearSVC(C=1.0, tol=0.0001, max_iter=1000, penalty='l2', loss='squared_hinge', dual=True, multi_class='ovr', fit_intercept=True, intercept

问题 I found sklearn.svm.LinearSVC and sklearn.svm.SVC(kernel='linear') and they seem very similar to me, but I get very different results on Reuters. sklearn.svm.LinearSVC: 81.05% in 28.87s train / 9.71s test sklearn.svm.SVC : 33.55% in 6536.53s train / 2418.62s test Both have a linear kernel. The tolerance of the LinearSVC is higher than the one of SVC: LinearSVC(C=1.0, tol=0.0001, max_iter=1000, penalty='l2', loss='squared_hinge', dual=True, multi_class='ovr', fit_intercept=True, intercept

问题 I am developing a image-classification project using BOW model and SVM. I want to find out the SVMs predict probability but there is no such function in opencv svm. Is there any way to do this? I want to find out the predict probability in n-class SVM. 回答1: No you can't do this with CvSVM. OpenCV's SVM implementation is based on a very old version of libsvm. Download the latest version of libsvm and use it instead. Of course you will have to write a wrapper to convert data formats. See http:/

问题 I am developing a image-classification project using BOW model and SVM. I want to find out the SVMs predict probability but there is no such function in opencv svm. Is there any way to do this? I want to find out the predict probability in n-class SVM. 回答1: No you can't do this with CvSVM. OpenCV's SVM implementation is based on a very old version of libsvm. Download the latest version of libsvm and use it instead. Of course you will have to write a wrapper to convert data formats. See http:/

问题 Trying to understand how SVM-OVR (One-Vs-Rest) works, I was testing the following code: import matplotlib.pyplot as plt import numpy as np from sklearn.svm import SVC x = np.array([[1,1.1],[1,2],[2,1]]) y = np.array([0,100,250]) classifier = SVC(kernel='linear', decision_function_shape='ovr') classifier.fit(x,y) print(classifier.predict([[1,2]])) print(classifier.decision_function([[1,2]])) The outputs are: [100] [[ 1.05322128 2.1947332 -0.20488118]] It means that the sample [1,2] is

问题 Trying to understand how SVM-OVR (One-Vs-Rest) works, I was testing the following code: import matplotlib.pyplot as plt import numpy as np from sklearn.svm import SVC x = np.array([[1,1.1],[1,2],[2,1]]) y = np.array([0,100,250]) classifier = SVC(kernel='linear', decision_function_shape='ovr') classifier.fit(x,y) print(classifier.predict([[1,2]])) print(classifier.decision_function([[1,2]])) The outputs are: [100] [[ 1.05322128 2.1947332 -0.20488118]] It means that the sample [1,2] is

问题 I'm new to scikit-learn, and SVM methods in general. I've got my data set working well with scikit-learn OneClassSVM in order to detect outliers; I train the OneClassSVM using observation all of which are 'inliers' and then use predict() to generate binary inlier/outlier predictions on my testing set of data. However to continue further with my analysis I'd like to get the probabilities associated with each new observation in my test set. E.g. The probability of being an outlier associated

问题 I am trying to get the variable importance for all predictors (or variables, or features) of a tuned support vector machine (svm) model using e1071::svm through the mlr -package in R . But I am not sure, if I am doing the assessment right. Well, at first the idea: To get an honest tuned svm-model, I am following the nested-resampling tutorial using spatial n-fold cross-validation ( SpRepCV ) in the outer loop and spatial cross-validation ( SpCV ) in the inner loop. As tuning parameter gamma