Python 的降維
Jeroen Boeye
Head of Machine Learning, Faktion
將要預測的特徵與用來訓練模型的特徵分開。
y = house_df['City'] X = house_df.drop('City', axis=1)
進行 70% 訓練、30% 測試的資料切分。
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
建立支援向量機分類器並擬合訓練資料。
from sklearn.svm import SVC svc = SVC() svc.fit(X_train, y_train)
from sklearn.metrics import accuracy_score print(accuracy_score(y_test, svc.predict(X_test)))
0.826
print(accuracy_score(y_train, svc.predict(X_train)))
0.832
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