2015年1月13日 · They are just different implementations of the same algorithm. The SVM module (SVC, NuSVC, etc) is a wrapper around the libsvm library and supports different kernels while LinearSVC is based on liblinear and only supports a linear kernel. So: SVC(kernel = 'linear') is in theory "equivalent" to: LinearSVC()

2017年1月11日 · fit an SVM model: from sklearn import svm svm = svm.SVC(gamma=0.001, C=100., kernel = 'linear') and implement the plot as follows: pd.Series(abs(svm.coef_[0]), index=features.columns).nlargest(10).plot(kind='barh') The resuit will be: the most contributing features of the SVM model in absolute values

2017年12月6日 · # Build your classifier classifier = svm.SVC() # Train it on the entire training data set classifier.fit(X_train, y_train) # Get predictions on the test set y_pred = classifier.predict(X_test) At this point, you can use any metric from the sklearn.metrics module to determine how well you did. For example:

2014年11月12日 · However to my knowledge, the SVM (scikit uses libsvm) should find this value. What's a good general range to test over (is there one?). For example, generally with C , a safe choice is 10^-5 ... 10^5, going up in exponential steps.

2021年1月21日 · try SVC(kernel='poly') and normalize your data . Compare your results against LogisticRegression() classifier.

sklearn.svm.SVC uses a pairwise (one-vs-one) decomposition by default and returns distances to all of the n(n-1)/2 hyperplanes for each sample. – Fred Foo Commented Nov 21, 2013 at 23:01

2014年11月17日 · Then use this Gram Matrix as the first argument (i.e. X) to svm.SVC().fit(): I start with the following code: C=0.1 model = svmTrain(X, y, C, "gaussian") that calls sklearn.svm.SVC() in svmTrain(), and then sklearn.svm.SVC().fit(): from sklearn import svm if kernelFunction == "gaussian": clf = svm.SVC(C = C, kernel="precomputed") return clf.fit ...

2013年2月22日 · svc = SVC(probability=True) preds_svc = svc.fit(X_train, y_train).predict(X_test) probs_svc = svc.decision_function(X_test)#The decision function tells us on which side of the hyperplane generated by the classifier we are (and how far we are away from it). probs_svc = (probs_svc - probs_svc.min()) / (probs_svc.max() - probs_svc.min())

Between SVC and LinearSVC, one important decision criterion is that LinearSVC tends to be faster to converge the larger the number of samples is. This is due to the fact that the linear kernel is a special case, which is optimized for in Liblinear, but not in Libsvm.

from sklearn.svm import LinearSVC from sklearn.calibration import CalibratedClassifierCV from sklearn import datasets #Load iris dataset iris = datasets.load_iris() X = iris.data[:, :2] # Using only two features y = iris.target #3 classes: 0, 1, 2 linear_svc = LinearSVC() #The base estimator # This is the calibrated classifier which can give ...