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Scikit-Learn NuSVC Model

NuSVC is a variant of the Support Vector Classification algorithm used for both binary and multi-class classification tasks.

It uses the parameter nu instead of C, which controls the number of support vectors and the training error margin. The key hyperparameters of NuSVC include nu, kernel, and gamma.

NuSVC is suitable for binary and multi-class classification problems.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.svm import NuSVC
from sklearn.metrics import accuracy_score

# generate synthetic dataset
X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=1)

# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# create model
model = NuSVC(nu=0.5, kernel='rbf', gamma='scale')

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
acc = accuracy_score(y_test, yhat)
print('Accuracy: %.3f' % acc)

# make a prediction
row = [[-1.10325445, -0.49821356, -0.05962247, -0.89224592, -0.70158632]]
yhat = model.predict(row)
print('Predicted: %d' % yhat[0])

Running the example gives an output like:

Accuracy: 0.950
Predicted: 0

The steps are as follows:

  1. First, a synthetic binary classification dataset is generated using the make_classification() function. This creates a dataset with a specified number of samples (n_samples), features (n_features), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().

  2. Next, a NuSVC model is instantiated with hyperparameters such as nu set to 0.5, kernel set to ‘rbf’, and gamma set to ‘scale’. The model is then fit on the training data using the fit() method.

  3. The performance of the model is evaluated by comparing the predictions (yhat) to the actual values (y_test) using the accuracy score metric.

  4. A single prediction can be made by passing a new data sample to the predict() method.

This example demonstrates how to set up and use a NuSVC model for classification tasks, showcasing its ability to handle both binary and multi-class problems effectively in scikit-learn.



See Also