SVM Support Vector Machines with Python derja

Informatique
Statistique
Merise
MCD
table de décision
MCT
compression
cryptographie
codification
enreprise
organisation Support vector machines (SVMs)
SVC, NuSVC and LinearSVC
sklearn
SVM: Maximum margin separating hyperplane,
Non-linear SVM
SVM-Anova: SVM with univariate feature selection,
Regression
decision_function(X)
Evaluate the decision function for the samples in X.
fit(X, y[, sample_weight])
Fit the SVM model according to the given training data.
get_params([deep])
Get parameters for this estimator.
predict(X)
Perform classification on samples in X.
predict_log_proba(X)
Compute log probabilities of possible outcomes for samples in X.
predict_proba(X)
Compute probabilities of possible outcomes for samples in X.
score(X, y[, sample_weight])
Return the mean accuracy on the given test data and labels.
set_params(**params)
Set the parameters of this estimator.