Correct preposition

05_support_vector_machines.ipynb

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-    "1. The fundamental idea behind Support Vector Machines is to fit the widest possible \"street\" between the classes. In other words, the goal is to have the largest possible margin between the decision boundary that separates the two classes of the training instances. When performing soft margin classification, the SVM searches for a compromise between perfectly separating the two classes and having the widest possible street (i.e., a few instances may end up on the street). Another key idea is to use kernels when training on nonlinear datasets. SVMs can also be tweaked to perform linear and nonlinear regression, as well as novelty detection.\n",
+    "1. The fundamental idea behind Support Vector Machines is to fit the widest possible \"street\" between the classes. In other words, the goal is to have the largest possible margin between the decision boundary that separates the two classes and the training instances. When performing soft margin classification, the SVM searches for a compromise between perfectly separating the two classes and having the widest possible street (i.e., a few instances may end up on the street). Another key idea is to use kernels when training on nonlinear datasets. SVMs can also be tweaked to perform linear and nonlinear regression, as well as novelty detection.\n",
     "2. After training an SVM, a _support vector_ is any instance located on the \"street\" (see the previous answer), including its border. The decision boundary is entirely determined by the support vectors. Any instance that is _not_ a support vector (i.e., is off the street) has no influence whatsoever; you could remove them, add more instances, or move them around, and as long as they stay off the street they won't affect the decision boundary. Computing the predictions with a kernelized SVM only involves the support vectors, not the whole training set.\n",
     "3. SVMs try to fit the largest possible \"street\" between the classes (see the first answer), so if the training set is not scaled, the SVM will tend to neglect small features (see Figure 5–2).\n",
     "4. You can use the `decision_function()` method to get confidence scores. These scores represent the distance between the instance and the decision boundary. However, they cannot be directly converted into an estimation of the class probability. If you set `probability=True` when creating an `SVC`, then at the end of training it will use 5-fold cross-validation to generate out-of-sample scores for the training samples, and it will train a `LogisticRegression` model to map these scores to estimated probabilities. The `predict_proba()` and `predict_log_proba()` methods will then be available.\n",