Understand the idea
RBF kernels support nonlinear boundaries. C controls the penalty/regularisation trade-off; gamma controls the locality of the kernel. Production searches C and keeps gamma="scale".
Python skill: Controls the penalty for training margin violations in the pipeline SVM.
Meet the syntax
GridSearchCV(model, {'model__C':[.5,2,10]}, scoring='f1_macro', cv=folds)'model__C'- Controls the penalty for training margin violations in the pipeline SVM.
scoring='f1_macro'- Judges settings by class-balanced validation evidence, not by margin magnitude.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
from sklearn.model_selection import GridSearchCV,StratifiedKFold
search=GridSearchCV(model,{'model__C':[.5,2,10]},cv=StratifiedKFold(5,shuffle=True,random_state=42),scoring='f1_macro').fit(X_train,y_train)
answer=search.cv_results_['mean_test_score']
This practice: Read and run the Python. Next: Change · Nonlinear SVM behaviour.
Given data · CLASS180
180 observations. Deterministic teaching observations; values illustrate the concept rather than a real population claim. The dataframe df is supplied afresh for each Run.
| 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 |
Column meanings and units
Column names describe the supplied features and target. Keep the stated units and row identities when making comparisons.
Synthetic data are deliberately small and reproducible. Their patterns illustrate an idea; they are not evidence about a real population.
| Column | Stored type |
|---|---|
| length | float64 |
| width | float64 |
| label | str |
Supplied setup · available if you need to inspect it
This code runs before your editor on every Run. These are the objects your exercise uses.
from sklearn.model_selection import train_test_split
X=df[['length','width']]
y=df.label
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,random_state=42,stratify=y)
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
model=Pipeline([('scale',StandardScaler()),('model',SVC(random_state=42))]).fit(X_train,y_train)
Your task · Follow
Search C=.5,2,10 using five stratified folds.
Hint 1 — Think
C changes the margin/error trade-off and must be chosen before final testing.
Hint 2 — Tools
GridSearchCV, StratifiedKFold and model__C.
Hint 3 — Approach
Evaluate the three declared C values with training-only macro-F1 folds.
Explained solution
from sklearn.model_selection import GridSearchCV,StratifiedKFold
search=GridSearchCV(model,{'model__C':[.5,2,10]},cv=StratifiedKFold(5,shuffle=True,random_state=42),scoring='f1_macro').fit(X_train,y_train)
answer=search.cv_results_['mean_test_score']
The shared folds compare regularisation choices while the pipeline keeps scaling within each fit.
Helpful prior knowledge: Margins and support vectors These links are guidance, not locks.
Sources and API context
Examples run with this Playground’s scikit-learn 1.4.2 / Pyodide 0.26.4 runtime.