SVM (Support Vector Machine) classifier is a supervised machine learning algorithm used for classification tasks. Here is a general overview of how to use an SVM classifier to classify data:

1. Data Preprocessing: Start by preparing your data. This includes cleaning, normalizing, and splitting your data into training and testing sets.

2. Model Selection: Choose the appropriate kernel function for your SVM classifier. Common kernel functions include linear, polynomial, and radial basis function (RBF).

3. Training the Model: Train your SVM classifier using the training data. The algorithm will find the optimal hyperplane that best separates the different classes in the feature space.

4. Model Evaluation: Evaluate the performance of your SVM classifier using the testing data. Common evaluation metrics include accuracy, precision, recall, and F1 score.

5. Prediction: Once the model is trained and evaluated, you can use it to make predictions on new, unseen data. The SVM classifier will assign a class label to each data point based on the learned decision boundary.

By following these steps, you can effectively use an SVM classifier to classify data in a variety of applications.
Support Vector Machines (SVMs) jsou supervizní algoritmy strojového učení používané pro klasifikační úlohy. Pro použití klasifikátoru SVM je nejprve datová sada rozdělena na trénovací a testovací sady. Trénovací data jsou poté použita k vytvoření modelu nalezením hyperrovin, které oddělují jednotlivé třídy. Nakonec je model testován na testovacích datech k vyhodnocení jeho přesnosti.