|
6 | 6 | from sklearn.svm import SVC |
7 | 7 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix |
8 | 8 | from sklearn.datasets import load_breast_cancer |
9 | | -data=load_breast_cancer() |
10 | | -X,y=data.data,data.target |
11 | 9 |
|
12 | | -X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42) |
| 10 | +data = load_breast_cancer() |
| 11 | +X, y = data.data, data.target |
13 | 12 |
|
14 | | -param_grid={ |
15 | | - 'C':[1, 10], |
16 | | - 'kernel':['linear','rbf'], |
17 | | - 'gamma':['scale'] |
18 | | -} |
| 13 | +X_train, X_test, y_train, y_test = train_test_split( |
| 14 | + X, y, test_size=0.3, random_state=42 |
| 15 | +) |
19 | 16 |
|
20 | | -svm=SVC(random_state=42) |
| 17 | +param_grid = {"C": [1, 10], "kernel": ["linear", "rbf"], "gamma": ["scale"]} |
21 | 18 |
|
22 | | -grid_search=GridSearchCV(svm, param_grid, cv=3, scoring='accuracy', n_jobs=-1, verbose=1) |
23 | | -grid_search.fit(X_train,y_train) |
| 19 | +svm = SVC(random_state=42) |
24 | 20 |
|
25 | | -print("Best parameters:",grid_search.best_params_) |
26 | | -print("Best cross validation score",grid_search.best_score_) |
| 21 | +grid_search = GridSearchCV( |
| 22 | + svm, param_grid, cv=3, scoring="accuracy", n_jobs=-1, verbose=1 |
| 23 | +) |
| 24 | +grid_search.fit(X_train, y_train) |
27 | 25 |
|
28 | | -best_svm=grid_search.best_estimator_ |
29 | | -y_pred=best_svm.predict(X_test) |
30 | | -test_accuracy=accuracy_score(y_test,y_pred) |
31 | | -print(f"Test accuracy:,{test_accuracy:.2f}") |
| 26 | +print("Best parameters:", grid_search.best_params_) |
| 27 | +print("Best cross validation score", grid_search.best_score_) |
32 | 28 |
|
| 29 | +best_svm = grid_search.best_estimator_ |
| 30 | +y_pred = best_svm.predict(X_test) |
| 31 | +test_accuracy = accuracy_score(y_test, y_pred) |
| 32 | +print(f"Test accuracy:,{test_accuracy:.2f}") |
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