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C-Support Vector Classification.
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Release Notes for SUSE Linux Enterprise Server 11 Service Pack 3 (SP3) This document provides guidance and an overview to high level general features and updates for SUSE Linux Enterprise Server 11 Service Pack 3 (SP3). Besides architecture or product-specific information, it also describes the capabilities and limitations of SLES 11 SP3. Receive emails with resources to guide you through your evaluation. After installation, install the latest servicing package. Go to: Microsoft update catalog and search for 'Windows Server 2019”. Evaluation versions of Windows Server must activate over the internet in the first 10 days to avoid automatic shutdown.
The implementation is based on libsvm. The fit time scales at leastquadratically with the number of samples and may be impracticalbeyond tens of thousands of samples. For large datasetsconsider using LinearSVC orSGDClassifier instead, possibly after aNystroem transformer.
The multiclass support is handled according to a one-vs-one scheme.
For details on the precise mathematical formulation of the providedkernel functions and how gamma, coef0 and degree affect eachother, see the corresponding section in the narrative documentation:Kernel functions.
Read more in the User Guide.
Regularization parameter. The strength of the regularization isinversely proportional to C. Must be strictly positive. The penaltyis a squared l2 penalty.
Specifies the kernel type to be used in the algorithm.It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ ora callable.If none is given, ‘rbf’ will be used. If a callable is given it isused to pre-compute the kernel matrix from data matrices; that matrixshould be an array of shape (n_samples,n_samples).
Degree of the polynomial kernel function (‘poly’).Ignored by all other kernels.
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.
if
gamma='scale'(default) is passed then it uses1 / (n_features * X.var()) as value of gamma,if ‘auto’, uses 1 / n_features.
Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.
Independent term in kernel function.It is only significant in ‘poly’ and ‘sigmoid’.
Whether to use the shrinking heuristic.See the User Guide.
Whether to enable probability estimates. This must be enabled priorto calling fit, will slow down that method as it internally uses5-fold cross-validation, and predict_proba may be inconsistent withpredict. Read more in the User Guide.
Tolerance for stopping criterion.
Specify the size of the kernel cache (in MB).
Set the parameter C of class i to class_weight[i]*C forSVC. If not given, all classes are supposed to haveweight one.The “balanced” mode uses the values of y to automatically adjustweights inversely proportional to class frequencies in the input dataas n_samples/(n_classes*np.bincount(y))
Enable verbose output. Note that this setting takes advantage of aper-process runtime setting in libsvm that, if enabled, may not workproperly in a multithreaded context.
Hard limit on iterations within solver, or -1 for no limit.
Whether to return a one-vs-rest (‘ovr’) decision function of shape(n_samples, n_classes) as all other classifiers, or the originalone-vs-one (‘ovo’) decision function of libsvm which has shape(n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one(‘ovo’) is always used as multi-class strategy. The parameter isignored for binary classification.
Changed in version 0.19: decision_function_shape is ‘ovr’ by default.
New in version 0.17: decision_function_shape=’ovr’ is recommended.
Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.
If true, decision_function_shape='ovr', and number of classes > 2,predict will break ties according to the confidence values ofdecision_function; otherwise the first class among the tiedclasses is returned. Please note that breaking ties comes at arelatively high computational cost compared to a simple predict.
Controls the pseudo random number generation for shuffling the data forprobability estimates. Ignored when probability is False.Pass an int for reproducible output across multiple function calls.See Glossary.
Multipliers of parameter C for each class.Computed based on the class_weight parameter.
The classes labels.
Weights assigned to the features (coefficients in the primalproblem). This is only available in the case of a linear kernel.
coef_ is a readonly property derived from dual_coef_ andsupport_vectors_.
Dual coefficients of the support vector in the decisionfunction (see Mathematical formulation), multiplied bytheir targets.For multiclass, coefficient for all 1-vs-1 classifiers.The layout of the coefficients in the multiclass case is somewhatnon-trivial. See the multi-class section of the User Guide for details.
0 if correctly fitted, 1 otherwise (will raise warning)
Constants in decision function.
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Indices of support vectors.
Support vectors.
Number of support vectors for each class.
If probability=True, it corresponds to the parameters learned inPlatt scaling to produce probability estimates from decision values.If probability=False, it’s an empty array. Platt scaling uses thelogistic function1/(1+exp(decision_value*probA_+probB_))where probA_ and probB_ are learned from the dataset [2]. Formore information on the multiclass case and training procedure seesection 8 of [1].
Array dimensions of training vector X.
See also
SVRSupport Vector Machine for Regression implemented using libsvm.
LinearSVCScalable Linear Support Vector Machine for classification implemented using liblinear. Check the See Also section of LinearSVC for more comparison element.
References
- 1
- 2
Examples
Methods
| Evaluates the decision function for the samples in X. |
| Fit the SVM model according to the given training data. |
| Get parameters for this estimator. |
| Perform classification on samples in X. |
| Return the mean accuracy on the given test data and labels. |
| Set the parameters of this estimator. |
decision_function(X)[source]¶Evaluates the decision function for the samples in X.
- Xarray-like of shape (n_samples, n_features)
Returns the decision function of the sample for each classin the model.If decision_function_shape=’ovr’, the shape is (n_samples,n_classes).
Notes
If decision_function_shape=’ovo’, the function values are proportionalto the distance of the samples X to the separating hyperplane. If theexact distances are required, divide the function values by the norm ofthe weight vector (coef_). See also this question for further details.If decision_function_shape=’ovr’, the decision function is a monotonictransformation of ovo decision function.
fit(X, y, sample_weight=None)[source]¶Fit the SVM model according to the given training data.
Training vectors, where n_samples is the number of samplesand n_features is the number of features.For kernel=”precomputed”, the expected shape of X is(n_samples, n_samples).
Target values (class labels in classification, real numbers inregression).
Per-sample weights. Rescale C per sample. Higher weightsforce the classifier to put more emphasis on these points.
- selfobject
Notes
If X and y are not C-ordered and contiguous arrays of np.float64 andX is not a scipy.sparse.csr_matrix, X and/or y may be copied.
If X is a dense array, then the other methods will not support sparsematrices as input.
get_params(deep=True)[source]¶Get parameters for this estimator.
If True, will return the parameters for this estimator andcontained subobjects that are estimators.
Parameter names mapped to their values.
predict(X)[source]¶Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
For kernel=”precomputed”, the expected shape of X is(n_samples_test, n_samples_train).
Class labels for samples in X.
predict_log_proba¶Compute log probabilities of possible outcomes for samples in X.
The model need to have probability information computed at trainingtime: fit with attribute probability set to True.
For kernel=”precomputed”, the expected shape of X is(n_samples_test, n_samples_train).
Returns the log-probabilities of the sample for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attribute classes_.
Notes
Kernel For Word Evaluation Version 11.01.01 Activation Code
The probability model is created using cross validation, sothe results can be slightly different than those obtained bypredict. Also, it will produce meaningless results on very smalldatasets.
predict_proba¶Compute probabilities of possible outcomes for samples in X.
The model need to have probability information computed at trainingtime: fit with attribute probability set to True.
For kernel=”precomputed”, the expected shape of X is(n_samples_test, n_samples_train).
Returns the probability of the sample for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attribute classes_.
Notes
The probability model is created using cross validation, sothe results can be slightly different than those obtained bypredict. Also, it will produce meaningless results on very smalldatasets.
score(X, y, sample_weight=None)[source]¶Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracywhich is a harsh metric since you require for each sample thateach label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Kernel For Word Evaluation Version 11.01.01 Activation Code 2017
Mean accuracy of self.predict(X) wrt. y.
set_params(**params)[source]¶Set the parameters of this estimator.
Free Evaluation Version
The method works on simple estimators as well as on nested objects(such as Pipeline). The latter haveparameters of the form <component>__<parameter> so that it’spossible to update each component of a nested object.
Kernel For Word Evaluation Version 11.01.01 Activation Code Download
Estimator parameters.
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Kernel For Word Evaluation Version 11.01.01 Activation Code List
Estimator instance.