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iOS小技能:苹果书签打包教程【WebClip描述文件(WebClip Configuration Profile)】

GG_lyf 03-03 15:00 阅读 2

一、模型评估

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# 使用测试集进行预测
y_pred = model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy*100:.2f}%")

# 打印混淆矩阵
conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(conf_matrix)

# 打印分类报告,包括精确率、召回率和F1分数
class_report = classification_report(y_test, y_pred)
print("Classification Report:")
print(class_report)

二、模型保存

#使用joblib保存模型
import joblib
joblib.dump(model, "./yorelee_model.pth")
#模型的后缀名是无所谓的

三、后话

%%time
# 2种模型融合
def model_mix(pred_1, pred_2):
    result = pd.DataFrame(columns=['LinearRegression','XGBRegressor','Combine'])

    for a in range (80):
        for b in range(1,80):
                    y_pred = (a*pred_1 + b*pred_2 ) / (a+b)
                    
                    mse = mean_squared_error(y_test,y_pred)
                    
                    mse = mean_squared_error(y_test,y_pred)
                    new_row = pd.DataFrame([{'LinearRegression':a, 
                                             'XGBRegressor':b, 
                                             'Combine':mse}])
                    result = pd.concat([result, new_row], ignore_index=True)
    return result

linear_predict=model_linear.predict(x_test)
xgb_predict=XGBClassifier.predict(x_test)
model_combine = model_mix(linear_predict,  xgb_predict)

model_combine.sort_values(by='Combine', inplace=True)
model_combine.head()
#各种比例来一份,看看mse最高分,查看 a和b的具体值
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