ML之xgboost:利用xgboost算法(sklearn+7CrVa)训练mushroom蘑菇数据集(22+1,6513+1611)来预测蘑菇是否毒性(二分类预测)
目录
- kfold = StratifiedKFold(n_splits=10, random_state=7)
- fit_params = {'eval_metric':"logloss"}
- results = cross_val_score(bst, X_train, y_train, cv=kfold, fit_params)
- results = cross_val_score(bst, X_train, y_train, cv=kfold)
- print(results)
- print("7-CrVa Accuracy Mean(STD): %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100)) 输出
-
-
- x = range(0,len(results))
- y1 = results
- y2 = [results.mean()]*10
- Xlabel = 'n_splits'
- Ylabel = 'Accuracy'
- title = 'mushroom datase: xgboost(sklearn+7CrVa) model'
-
- plt.plot(x,y1,'g') 绘制曲线
- plt.plot(x,y2,'r--') 平均值曲线
- plt.xlabel(Xlabel)
- plt.ylabel(Ylabel)
- plt.title(title)
- plt.show()
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