鏀寔鍚戦噺鏈?SVM - Wenjing
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鏁板鐞嗚
璇勪及鎸囨爣
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from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
X = df_feat
y = cancer['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = SVC()
model.fit(X_train, y_train)
pred = model.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(y_test, pred))
print(confusion_matrix(y_test, pred))
- 浣跨敤Gri 澶т笓鏍?/a> 鏀寔鍚戦噺鏈?SVM - WenjingdSearchCV杩涜鑷姩璋冨弬
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from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
param_grid = {'C': [0.1, 1, 10, 100, 1000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001]}
grid = GridSearchCV(SVC(), param_grid, verbose=3)
grid.fit(X_train, y_train)
grid.best_params_
grid.best_estimator_
grid_pred = grid.predict(X_test)
print(classification_report(y_test, grid_pred))
print(confusion_matrix(y_test, grid_pred))
鏁板鐭ヨ瘑琛ュ厖
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from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
X = df_feat
y = cancer['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = SVC()
model.fit(X_train, y_train)
pred = model.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(y_test, pred))
print(confusion_matrix(y_test, pred))
1 | from sklearn.model_selection import train_test_split |