Margins and support vectors · Data Playground

Understand the idea

SVM fits boundaries influenced by support vectors near margins. Production SVC uses an RBF kernel and does not enable probability estimation. Decision values are not probabilities. An RBF kernel allows a curved boundary in measurement space. This activity inspects a supplied fitted classifier; Apply builds and evaluates the complete recipe.

Illustrative data

Illustrative data
Column	Values
Class A	(0, 0); (0.5, 0); (-0.5, 0); (0, 0.5); (0, -0.5)
Class B	(1.5, 0); (-1.5, 0); (0, 1.5); (0, -1.5); (1, 1); (-1, -1); (1, -1); (-1, 1)

Click the highlighted code to change the result.

example_model = SVC(kernel="rbf", C=, gamma=1)
example_model.fit(example_X_prepared, example_y)
example_model.n_support_  # A: 4, B: 8

C=1, gamma=1: support A=4, B=8. The zero contour separates predicted regions. Rings identify the fitted support vectors; decision scores are not probabilities.

What the syntax does

SVC(kernel='rbf', C=1.0, gamma='scale'): Creates a support vector classifier.
C: 0.5, 1, 2, 10
gamma: "scale", "auto", 0.1, 1, 10
kernel: "linear", "rbf", "poly", "sigmoid", "precomputed", kernel_function
kernel accepts named choices such as linear and rbf (the default).
C must be positive; larger values penalise margin violations more strongly. gamma=scale uses fitted feature variation; a positive numeric gamma sets locality explicitly.
Keep scaling and parameter selection within training folds.
model.decision_function(X_test): Returns signed margin-based decision values; these are not probabilities.

Supplied for this practice

These objects are prepared afresh before your editor runs.

X: Feature dataframe for the declared population, preserving row indices.
X = df[['length', 'width']]
y: Target series, aligned with X.
y = df.label
X_train / X_test / y_train / y_test: Training feature rows from the declared split. Final-test feature rows from the declared split. Training targets, aligned with X_train. Final-test targets, aligned with X_test.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
model: Already fitted on X_train, y_train. Fitted estimator or pipeline for the requested model family. Fitted on X_train, y_train; reuse without refitting.

Outputs you create

answer: The requested result, with the values, labels and row order described by this task.

CLASS180 · first 8 rows
`length`	`width`	`label`
236.033	-0.805568	A
581.297	0.728558	A
-1511.27	-1.00866	A
99.0248	-0.24496	A
-13.0141	-0.660765	A
681.179	0.602475	A
51.1472	0.873157	A
362.131	-0.665605	A

Your task

Use the supplied fitted scaled SVM to predict held-away labels.

answer

Hints

Think: The supplied fitted pipeline already contains the scale and margin model.

Tools: Pipeline.predict.

Approach: Pass the held-away features through the existing fitted pipeline.

Explained solution

answer=model.predict(X_test)

Prediction applies the learned preparation and SVM decision rule without refitting on evaluation rows.