graphlab.svm_classifier.create(dataset, target, features=None, penalty=1.0, solver='auto', feature_rescaling=True, convergence_threshold=0.01, lbfgs_memory_level=11, max_iterations=10, class_weights=None, validation_set='auto', verbose=True)

Create a SVMClassifier to predict the class of a binary target variable based on a model of which side of a hyperplane the example falls on. In addition to standard numeric and categorical types, features can also be extracted automatically from list- or dictionary-type SFrame columns.

This loss function for the SVM model is the sum of an L1 mis-classification loss (multiplied by the ‘penalty’ term) and a l2-norm on the weight vectors.


dataset : SFrame

Dataset for training the model.

target : string

Name of the column containing the target variable. The values in this column must be of string or integer type. String target variables are automatically mapped to integers in alphabetical order of the variable values. For example, a target variable with ‘cat’ and ‘dog’ as possible values is mapped to 0 and 1 respectively with 0 being the base class and 1 being the reference class.

features : list[string], optional

Names of the columns containing features. ‘None’ (the default) indicates that all columns except the target variable should be used as features.

The features are columns in the input SFrame that can be of the following types:

  • Numeric: values of numeric type integer or float.
  • Categorical: values of type string.
  • Array: list of numeric (integer or float) values. Each list element is treated as a separate feature in the model.
  • Dictionary: key-value pairs with numeric (integer or float) values Each key of a dictionary is treated as a separate feature and the value in the dictionary corresponds to the value of the feature. Dictionaries are ideal for representing sparse data.

Columns of type list are not supported. Convert them to array in case all entries in the list are of numeric types and separate them out into different columns if they are of mixed type.

penalty : float, optional

Penalty term on the mis-classification loss of the model. The larger this weight, the more the model coefficients shrink toward 0. The larger the penalty, the lower is the emphasis placed on misclassified examples, and the classifier would spend more time maximizing the margin for correctly classified examples. The default value is 1.0; this parameter must be set to a value of at least 1e-10.

solver : string, optional

Name of the solver to be used to solve the problem. See the references for more detail on each solver. Available solvers are:

  • auto (default): automatically chooses the best solver (from the ones listed below) for the data and model parameters.
  • lbfgs: lLimited memory BFGS (lbfgs) is a robust solver for wide datasets(i.e datasets with many coefficients).

The solvers are all automatically tuned and the default options should function well. See the solver options guide for setting additional parameters for each of the solvers.

feature_rescaling : bool, default = true

Feature rescaling is an important pre-processing step that ensures that all features are on the same scale. An l2-norm rescaling is performed to make sure that all features are of the same norm. Categorical features are also rescaled by rescaling the dummy variables that are used to represent them. The coefficients are returned in original scale of the problem.

convergence_threshold :

Convergence is tested using variation in the training objective. The variation in the training objective is calculated using the difference between the objective values between two steps. Consider reducing this below the default value (0.01) for a more accurately trained model. Beware of overfitting (i.e a model that works well only on the training data) if this parameter is set to a very low value.

max_iterations : int, optional

The maximum number of allowed passes through the data. More passes over the data can result in a more accurately trained model. Consider increasing this (the default value is 10) if the training accuracy is low and the Grad-Norm in the display is large.

lbfgs_memory_level : int, optional

The L-BFGS algorithm keeps track of gradient information from the previous lbfgs_memory_level iterations. The storage requirement for each of these gradients is the num_coefficients in the problem. Increasing the lbfgs_memory_level can help improve the quality of the model trained. Setting this to more than max_iterations has the same effect as setting it to max_iterations.

class_weights : {dict, auto}, optional

Weights the examples in the training data according to the given class weights. If set to None, all classes are supposed to have weight one. The auto mode set the class weight to be inversely proportional to number of examples in the training data with the given class.

validation_set : SFrame, optional

A dataset for monitoring the model’s generalization performance. For each row of the progress table, the chosen metrics are computed for both the provided training dataset and the validation_set. The format of this SFrame must be the same as the training set. By default this argument is set to ‘auto’ and a validation set is automatically sampled and used for progress printing. If validation_set is set to None, then no additional metrics are computed. The default value is ‘auto’.

verbose : bool, optional

If True, print progress updates.


out : SVMClassifier

A trained model of type SVMClassifier.

See also



  • Categorical variables are encoded by creating dummy variables. For a variable with \(K\) categories, the encoding creates \(K-1\) dummy variables, while the first category encountered in the data is used as the baseline.
  • For prediction and evaluation of SVM models with sparse dictionary inputs, new keys/columns that were not seen during training are silently ignored.
  • The penalty parameter is analogous to the ‘C’ term in the C-SVM. See the reference on training SVMs for more details.
  • Any ‘None’ values in the data will result in an error being thrown.
  • A constant term of ‘1’ is automatically added for the model intercept to model the bias term.
  • Note that the hinge loss is approximated by the scaled logistic loss function. (See user guide for details)



Given an SFrame sf, a list of feature columns [feature_1 ... feature_K], and a target column target with 0 and 1 values, create a SVMClassifier as follows:

>>> data =  graphlab.SFrame('')
>>> data['is_expensive'] = data['price'] > 30000
>>> model = graphlab.svm_classifier.create(data, 'is_expensive')