scikit-learn Cookbook , Second Edition - Second Edition

How to do it...

The process of finding the SVM line is straightforward. It is the same process as with any scikit-learn supervised learning estimator:

1. Create training and testing sets.
2. Create an SVM model instance.
3. Fit the SVM model to the loaded data.
4. Predict with the SVM model and measure the performance of the model in preparation for predictions of unseen data.

Let's begin:

1. Split the dataset of the first two features of the first two classes. Stratify the target set:

from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7,stratify=y)

2. Create an SVM model instance. Set the kernel to be linear, as we want a line to separate the two classes that are involved ...