Scikit-Learn SVC Model

Support Vector Classification (SVC) is a powerful algorithm for binary and multi-class classification problems. It aims to find the optimal separating hyperplane that maximizes the margin between different classes.

The key hyperparameters of SVC include the C parameter (regularization strength), kernel (type of kernel function), and gamma (kernel coefficient). C controls the trade-off between achieving a low error on the training data and minimizing the norm of the weights, kernel specifies the kernel type to be used in the algorithm, and gamma defines how far the influence of a single training example reaches.

SVC is suitable for classification tasks where the goal is to classify data into distinct categories.

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

# generate binary classification 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 = SVC()

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

2. Instantiate an SVC model with default hyperparameters. Fit the model on the training data using the fit() method.

3. Evaluate the performance of the model by predicting the test set and calculating the accuracy score. The predictions (yhat) are compared to the actual values (y_test).

4. Make a prediction using a new data sample by passing it to the predict() method.

This example demonstrates how to set up and use an SVC model for binary classification tasks, showcasing the simplicity and effectiveness of this algorithm in scikit-learn.

The model can be fit directly on the training data and used to make predictions on new data, enabling its use in real-world classification problems.