上传文件至 /

function.py

@@ -1,4 +1,5 @@
 import numpy as np
+from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import PolynomialFeatures, StandardScaler
 
@@ -30,17 +31,30 @@ def preprocess_data(X):
     return X_poly
 
 def train_logreg(X, y):
-    X_processed = preprocess_data(X)  # 预处理训练数据
     model = RandomForestClassifier(n_estimators=113, max_depth=10, random_state=42)
-    model.fit(X_processed, y)
-    return model  # 返回训练好的模型
+    model.fit(X, y)
+    return model
 
-def predict_logreg(X, model):
-    # 确保输入数据是一个 numpy 数组
-    if len(X.shape) == 1:
-        X = X.reshape(1, -1)  # 保持输入数据的形状为 (1, n)，符合模型的输入要求
-    X_processed = preprocess_data(X)  # 预处理测试数据
-    return model.predict(X_processed)[0]  # 确保 model 是训练好的模型
+def predict_logreg(model, X):
+    return model.predict(X)
 
 if __name__ == "__main__":
+    # 加载数据
     X, y = load_data("./data/horse-colic.data")
+
+    # 划分数据集
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # 数据预处理
+    X_train_processed = preprocess_data(X_train)
+    X_test_processed = preprocess_data(X_test)
+
+    # 训练模型
+    model = train_logreg(X_train_processed, y_train)
+
+    # 预测
+    y_pred = predict_logreg(model, X_test_processed)
+
+    # 计算正确率
+    accuracy = np.mean(y_pred == y_test)
+    print(f"测试出的正确率: {accuracy * 100:.2f}%")