解释最优：0.8204777025809096 使用{'n_estimators': 17} 0.580502 (0.199044) with {'n_estimators': 1} 0.681356 (0.194808) with {'n_estimators': 2} 0.746717 (0.098332) with {'n_estimators': 3} 0.729439 (0.148258) with {'n_estimators': 4} 0.795942 (0.074020) with {'n_estimators': 5} 0.792932 (0.074212) with {'n_estimators': 6} 0.790831 (0.081197) with {'n_estimators': 7} 0.806563 (0.069099) with {'n_estimators': 8} 0.804305 (0.089146) with {'n_estimators': 9} 0.796618 (0.099442) with {'n_estimators': 10} 0.796024 (0.084307) with {'n_estimators': 11} 0.810367 (0.092068) with {'n_estimators': 12} 0.813586 (0.082389) with {'n_estimators': 13} 0.801124 (0.096973) with {'n_estimators': 14} 0.816928 (0.086265) with {'n_estimators': 15} 0.806749 (0.087071) with {'n_estimators': 16} 0.820478 (0.090866) with {'n_estimators': 17} 0.814466 (0.081672) with {'n_estimators': 18} 0.799598 (0.105779) with {'n_estimators': 19} 0.814981 (0.087164) with {'n_estimators': 20} 0.816971 (0.085249) with {'n_estimators': 21}

Python rolldecay_estimators库及其安装教程

资源摘要信息: "Python库 | rolldecay_estimators-0.0.3-py3-none-any.whl" 1. Python库概念 Python库是一系列预编写的代码，允许开发者在开发程序时重用现有的功能。这些代码通常被组织成模块、包和子包，为Python...

TensorFlow Estimators：简化与灵活性在高级机器学习框架中的管理

"TensorFlow Estimators是TensorFlow框架中的高级机器学习接口，旨在简化深度学习模型的构建、训练、评估和部署过程。它提供了一种在生产环境中实施尖端机器学习技术的方法，同时考虑到了灵活性和易用性之间的平衡。...

rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500) rf.fit(train_vectors, newsgroups_train.target)

其中，n_estimators参数指定了随机森林中决策树的数量。接下来，训练集的向量表示train_vectors和对应的标签newsgroups_train.target被用来训练这个分类器模型。训练完成后，可以使用这个分类器模型对测试集进行预测...

以下代码是哪出现了问题呢？为什么运行报错“subsample”：from sklearn.model_selection import cross_val_score from hyperopt import hp, fmin, tpe, Trials from xgboost import XGBRegressor as XGBR data = pd.read_csv(r"E:\exercise\synthesis\synthesis_dummy_2.csv") #验证随机森林填补缺失值方法是否有效 X = data.iloc[:,1:] y = data.iloc[:,0] # 定义超参数空间min_child_weight在0~40;num_boost_round的范围可以定到range(1,100,2);gamma在[20,100];lambda范围[1,2]; space = { 'max_depth': hp.choice('max_depth', range(1, 30)), 'n_estimators':hp.quniform("n_estimators",1,100), 'learning_rate':hp.uniform('subsample', 0.1, 1), 'min_child_weight': hp.choice('min_child_weight', range(1, 40)), 'gamma': hp.uniform('gamma', 1, 100), 'subsample': hp.uniform('subsample', 0.1, 1), 'colsample_bytree': hp.uniform('colsample_bytree', 0.1, 1) } # 定义目标函数 def hyperopt_objective(params): reg = XGBR(random_state=100, params) scores = cross_val_score(reg, Xtrain, Ytrain, cv=5) # 五倍交叉验证 return 1 - scores.mean() # 返回平均交叉验证误差的相反数，即最小化误差 # 创建Trials对象以记录调参过程 trials = Trials() # 使用贝叶斯调参找到最优参数组合 best = fmin(hyperopt_objective, space, algo=tpe.suggest, max_evals=100, trials=trials) # 输出最优参数组合 print("Best parameters:", best) # 在最优参数组合下训练模型 best_params = space_eval(space, best) reg = XGBR(random_state=100, best_params) reg.fit(Xtrain, Ytrain) # 在验证集上评估模型 y_pred = reg.predict(X_val) evaluation = evaluate_model(y_val, y_pred) # 自定义评估函数 print("Model evaluation:", evaluation)

'n_estimators': hp.quniform("n_estimators", 1, 100), 'learning_rate': hp.uniform('learning_rate', 0.1, 1), # 将'subsample'改为'learning_rate' 'min_child_weight': hp.choice('min_child_weight', range...

model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=42) for i in range(model.n_estimators): model.fit(X_train, y_train) fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(4, 4), dpi=300) plot_tree(model.estimators_[i], filled=True) plt.savefig(r'picture/picture_{}.png'.format(i), plot_tree(i)) plt.show()是否有问题？

最后，如果想要将所有的树可视化保存，可以使用 for 循环遍历所有的树，而不是只遍历模型中的 n_estimators 棵树。修正后的代码可以参考下面的实现： model = RandomForestClassifier(n_estimators=10, max_...

Y = df_dummies['睡眠障碍'] Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size = 0.3) rfc = RandomForestClassifier().fit(Xtrain,Ytrain) print(rfc.score(Xtest,Ytest)) test_scores = [] n_estimators = range(150,200,1) Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size = 0.3) for n in n_estimators: rfc = RandomForestClassifier( n_estimators=n ).fit(Xtrain,Ytrain) test_scores.append(cross_val_score(rfc,Xtest,Ytest,cv =10).mean()) px.line( x = n_estimators, y = test_scores )

这是一个基于随机森林分类器的机器学习...通过调整n_estimators参数来寻找最佳模型，cross_val_score函数用于交叉验证模型的准确率。px.line函数将不同n_estimators下的测试准确率绘制成折线图，用于模型选择和调参。

解释from sklearn.ensemble import RandomForestClassifier # Create the model with 200 trees RF_model = RandomForestClassifier(n_estimators=200, bootstrap = True, max_features = 'sqrt') # Fit on training data RF_model.fit(X_train_split,y_train_split) # Actual class predictions tr_predictions = RF_model.predict(X_train_split) rf_predictions = RF_model.predict(X_val) # Probabilities for each class print('平均分类准确率为：\n',accuracy_score(y_train_split,np.round(tr_predictions))) print('平均分类准确率为：\n',accuracy_score(y_val,np.round(rf_predictions)))

这段代码是使用Python中的scikit-learn库中的随机森林分类器模型进行训练和预测。首先，通过从sklearn.ensemble中导入RandomForestClassifier类，创建一个包含200个决策树的随机森林模型。其中，bootstrap=True表示...

优化代码# GBDT 模型的网格搜索法 # 选择不同的参数 from sklearn.model_selection import GridSearchCV learning_rate_options = [0.01, 0.05, 0.1] max_depth_options = [3,5,7,9] n_estimators_options = [100, 300, 500] parameters = {'learning_rate':learning_rate_options, 'max_depth':max_depth_options, 'n_estimators':n_estimators_options} grid_gbdt = GridSearchCV(estimator= GradientBoostingClassifier(),param_grid=parameters,cv=10,scoring='accuracy') grid_gbdt.fit(X_train, y_train) # 结果输出 grid_gbdt.grid_scores_,grid_gbdt.best_params_, grid_gbdt.best_score_，一直运行无法显示结果

您可以尝试将最后一行的输出改为以下内容： print(grid_gbdt.cv_results_) print(grid_gbdt.best_params_) print(grid_gbdt.best_score_) ...您可以尝试减少参数组合或使用更小的数据集进行实验。

将下面这段代码进行修改调制融入我的代码中：best_accuracy = 0.0 best_params = {} # Iterate over different parameter combinations for n_estimators in [10, 30, 50]: for max_depth in [2, 4, 6]: for max_features in ['sqrt', 'log2']: for min_samples_split in [2, 4, 6]: # Train a RandomForestClassifier with the current parameter combination rf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, max_features=max_features, min_samples_split=min_samples_split) rf.fit(X_train, Y_train) # Predict using the trained model Y_pred = rf.predict(X_test) # Compute accuracy current_accuracy = accuracy_score(Y_test, Y_pred, normalize=True) # Check if the current accuracy is higher than the best accuracy so far if current_accuracy > best_accuracy: best_accuracy = current_accuracy best_params = { 'n_estimators': n_estimators, 'max_depth': max_depth, 'max_features': max_features, 'min_samples_split': min_samples_split } print("Best Accuracy:", best_accuracy) print("Best Parameters:", best_params)

在这个例子中，我们使用了三个参数：num_epochs（训练轮数）、learning_rate（学习率）和batch_size（批量大小），并通过交叉验证的方式寻找最佳超参数组合。你可以根据你的实际情况，修改这些参数和参数值，以及...

from xgboost import XGBRegressor tuned_parameters = [{ 'max_depth': range(3,10), 'n_estimators': range(100, 600, 100), 'learning_rate':[0.01] },] # 非GPU xgb= GridSearchCV(estimator=XGBRegressor(), param_grid=tuned_parameters, cv=5) # GPU # xgb= GridSearchCV(estimator=XGBRegressor(tree_method='gpu_hist', gpu_id=0), param_grid=tuned_parameters, cv=5) # 也可以换成lgbm,lgbm比xgboost快很多 # xgb= GridSearchCV(estimator=LGBMRegressor(), param_grid=tuned_parameters, cv=5) xgb.fit(XX_train,YY_train) y_xgb= xgb.predict(XX_test) print ('Optimum epsilon and kernel 1D: ', xgb.best_params_) # evaluate predictions mae = mean_absolute_error(YY_test, y_xgb) mape = mean_absolute_percentage_error(YY_test['BOD'], y_xgb) score = xgb.score(XX_test, YY_test) train_score = xgb.score(XX_train, YY_train) print('MAE: %.3f, MAPE: %.3f, R2_tain: %.3f, R2_test: %.3f' % ((mae,mape,train_score,score)))

这段代码是用来进行XGBoost模型的超...其中，可以选择使用GPU进行计算以提高速度，也可以选择使用LightGBM模型代替XGBoost模型。最后打印出最佳参数组合，并输出模型在测试集上的MAE、MAPE、R2_train和R2_test等指标。

逐行解释下面的代码：from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split, GridSearchCV, KFold from sklearn.ensemble import RandomForestClassifier data = load_breast_cancer() X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42) kf = KFold(n_splits=5, shuffle=True, random_state=42) param_grid = {'n_estimators': range(1, 21, 1), 'max_depth': range(5, 16)} rf = RandomForestClassifier(random_state=42) grid_search = GridSearchCV(rf, param_grid=param_grid, cv=kf, n_jobs=-1) grid_search.fit(X_train, y_train) best_rf = RandomForestClassifier(n_estimators=grid_search.best_params_['n_estimators'], max_depth=grid_search.best_params_['max_depth'], random_state=42) best_rf.fit(X_train, y_train) y_pred = best_rf.predict(X_test)

接下来，定义一个字典param_grid，其中包含了随机森林算法的两个参数：n_estimators和max_depth。n_estimators参数表示随机森林中决策树的数量，max_depth参数表示每个决策树的最大深度。param_grid的取值范围分别为...

根据以下代码，利用shap库写出绘制bar plot图的代码“def five_fold_train(x: pd.DataFrame, y: pd.DataFrame, model_class: type, super_parameters: dict = None, return_model=False): """ 5折交叉验证训练器 :param x: :param y: :param model_class: 学习方法类别，传入一个类型 :param super_parameters: 超参数 :param return_model: 是否返回每个模型 :return: list of [pred_y,val_y,auc,precision,recall] """ res = [] models = [] k_fold = KFold(5, random_state=456, shuffle=True) for train_index, val_index in k_fold.split(x, y): #即对数据进行位置索引，从而在数据表中提取出相应的数据 train_x, train_y, val_x, val_y = x.iloc[train_index], y.iloc[train_index], x.iloc[val_index], y.iloc[val_index] if super_parameters is None: super_parameters = {} model = model_class(**super_parameters).fit(train_x, train_y) pred_y = model.predict(val_x) auc = metrics.roc_auc_score(val_y, pred_y) precision = metrics.precision_score(val_y, (pred_y > 0.5) * 1) recall = metrics.recall_score(val_y, (pred_y > 0.5) * 1) res.append([pred_y, val_y, auc, precision, recall]) models.append(model) # print(f"fold: auc{auc} precision{precision} recall{recall}") if return_model: return res, models else: return res best_params = { "n_estimators": 500, "learning_rate": 0.05, "max_depth": 6, "colsample_bytree": 0.6, "min_child_weight": 1, "gamma": 0.7, "subsample": 0.6, "random_state": 456 } res, models = five_fold_train(x, y, XGBRegressor, super_parameters=best_params, return_model=True)”

model_index = 0 # 获取特征重要性信息 explainer = shap.TreeExplainer(models[model_index]) shap_values = explainer.shap_values(x) # 绘制bar plot shap.summary_plot(shap_values, x, plot_type="bar") # ...

以下代码是什么意思：oob_score = [] for item in grid_n: model = RandomForestClassifier(n_estimators=item, random_state=10, oob_score=True) model.fit(X_train, y_train) oob_score.append(model.oob_score_) grid_n = [20, 50, 100, 150, 200, 500] grid_fea = np.arange(2, 19) grid_weight = ['balanced', None] model_RF = RandomForestClassifier(random_state=10) grid_search = GridSearchCV(estimator=model_RF, param_grid={'n_estimators':grid_n, 'max_features':grid_fea, 'class_weight':grid_weight}, cv=5, scoring='roc_auc') grid_search.fit(X_train, y_train) grid_search.best_params_ y_prob_rf = grid_search.predict_proba(X_test)[:, 1] y_pred_rf = grid_search.predict(X_test) print(classification_report(y_pred=y_pred_rf, y_true=y_test)) fpr, tpr, threshold = roc_curve(y_score=y_prob_rf, y_true=y_test) print('AUC值：', auc(fpr, tpr)) plt.plot(fpr, tpr, 'r-') plt.plot([0, 1], [0, 1], 'b--') plt.xlabel('FPR') plt.ylabel('TPR') plt.title('ROC Curve') best_RF = grid_search.best_estimator_ best_RF.fit(X_train, y_train) plt.figure(figsize=(8, 6)) pd.Series(best_RF.feature_importances_, index=X_train.columns).sort_values().plot(kind='barh')

首先，它定义了一些参数的取值范围，包括树的数量（n_estimators）、最大特征数（max_features）和类别权重（class_weight）。然后，使用这些参数值调用GridSearchCV函数，对模型进行交叉验证并寻找最佳参数组合。接...

解释这段代码：# 决策树 dt = DecisionTreeClassifier(max_depth=5, random_state=0) dt.fit(X_train, y_train) y_pred_dt = dt.predict(X_test) print('决策树准确率：', metrics.accuracy_score(y_test, y_pred_dt)) # 决策树可视化 dot_data = export_graphviz(dt, out_file=None, feature_names=X_train.columns, class_names=['Dead', 'Survived'], filled=True, rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph.render('titanic_decision_tree') # 剪枝 dt_pruned = DecisionTreeClassifier(max_depth=5, ccp_alpha=0.01, random_state=0) dt_pruned.fit(X_train, y_train) y_pred_pruned = dt_pruned.predict(X_test) print('剪枝决策树准确率：', metrics.accuracy_score(y_test, y_pred_pruned)) # 随机森林 rf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0) rf.fit(X_train, y_train) y_pred_rf = rf.predict(X_test) print('随机森林准确率：', metrics.accuracy_score(y_test, y_pred_rf))

首先，使用DecisionTreeClassifier函数构建一个决策树模型，设置最大深度为5，随机种子为0，并使用X_train和y_train训练模型，使用X_test预测结果并计算准确率。然后，使用export_graphviz函数将决策树可视化，设置...

WARNING: C:\Users\dev-admin\croot2\xgboost-split_1675461376218\work\src\learner.cc:767: Parameters: { "n_estimators" } are not used.

这个warning提示你设置了一个参数n_estimators，但是它并没有被使用。这个参数是XGBoost库中Gradient Boosting算法的参数，而不是Random Forest算法的参数。在Gradient Boosting算法中，树的数量是通过num_boost_...

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 构造随机森林模型 model = RandomForestClassifier(n_estimators=5, max_depth=5, random_state=42) for i in range(model.n_estimators): model.fit(X_train, y_train) # 训练模型 fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 8), dpi=300) plot_tree(model.estimators_[i], filled=True) # plt.savefig(r'D:\pythonProject1\picture/picture_{}.png'.format(i), format='png') #保存图片 plt.show() # 在测试集上评估模型的性能 y_pred = model.predict(X_test)

这段代码使用sklearn库中的train_test_split函数将数据集X和标签y划分为训练集和测试集，其中测试集的大小是原始数据集的20%。然后，使用sklearn库中的RandomForestClassifier类构造一个随机森林模型，并使用循环...

from sklearn.metrics import roc_curve clf1 = lgb.LGBMClassifier(max_depth= 13, n_estimators= 400) clf2 = RandomForestClassifier(criterion='entropy', max_depth=19, n_estimators=500) clf3 = xgb.XGBClassifier(max_depth= 8, n_estimators= 100) lr = LogisticRegression(max_iter=2000,C= 10, penalty='l1', solver= 'liblinear') logis_fpr, logis_tpr, logis_threshoulds = roc_curve(test_y, logist_gs.best_estimator_.predict_proba(test_x)) print(logis_fpr)

这段代码使用了 scikit-learn 库中的 roc_curve 函数来计算逻辑回归模型的 ROC 曲线。在此之前，代码中定义了三个分类器 clf1、clf2 和 clf3，以及一个逻辑回归模型 lr，并对它们进行了一些参数设置。test_x 和 test...

以下这段代码中的X_val、y_val是来自哪儿呢，没有看到有X和Y的对训练集和测试集的划分的代码，并且这段代码还报错”name 'space_eval' is not defined“，且Xtrain,Xtest,Ytrain,Ytest = TTS(X, y,test_size=0.2,random_state=100)只划分了训练集和测试集，验证集是在哪呢？还有一个问题是以下代码用了五倍交叉验证，所以不需要用这段代码"Xtrain,Xtest,Ytrain,Ytest = TTS(X, y,test_size=0.2,random_state=100)”来划分训练集和测试集了吗：from sklearn.model_selection import cross_val_score from hyperopt import hp, fmin, tpe, Trials from xgboost import XGBRegressor as XGBR # 定义超参数空间 space = { 'max_depth': hp.choice('max_depth', range(1, 10)), 'min_child_weight': hp.choice('min_child_weight', range(1, 10)), 'gamma': hp.choice('gamma', [0, 1, 5, 10]), 'subsample': hp.uniform('subsample', 0.5, 1), 'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1) } # 定义目标函数 def hyperopt_objective(params): reg = XGBR(random_state=100, n_estimators=22, params) scores = cross_val_score(reg, X_train, y_train, cv=5) # 五倍交叉验证 return 1 - scores.mean() # 返回平均交叉验证误差的相反数，即最小化误差 # 创建Trials对象以记录调参过程 trials = Trials() # 使用贝叶斯调参找到最优参数组合 best = fmin(hyperopt_objective, space, algo=tpe.suggest, max_evals=100, trials=trials) # 输出最优参数组合 print("Best parameters:", best) # 在最优参数组合下训练模型 best_params = space_eval(space, best) reg = XGBR(random_state=100, n_estimators=22, best_params) reg.fit(X_train, y_train) # 在验证集上评估模型 y_pred = reg.predict(X_val) evaluation = evaluate_model(y_val, y_pred) # 自定义评估函数 print("Model evaluation:", evaluation)

1. 代码中的X_val和y_val是用于在最优参数组合下训练模型后，在验证集上评估模型的数据。在给定的代码中，并没有显示从哪里获取这些数据。可能是在代码的其他部分进行了训练集、验证集和测试集的划分，并将X_...

def xgb_cv(max_depth, learning_rate, n_estimators, gamma, min_child_weight, subsample, colsample_bytree): date_x = pd.read_csv('Train_data1.csv') # Well logging data date_x.rename(columns={"TC": 'label'}, inplace=True) date_x.drop('Depth', axis=1, inplace=True) date_x.drop('MSFL', axis=1, inplace=True) date_x.drop('CNL', axis=1, inplace=True) date_x.drop('AC', axis=1, inplace=True) date_x.drop('GR', axis=1, inplace=True) data = date_x.iloc[2:42, :] label = data.iloc[:, 1:2] data2 = data.iloc[:, :7] train_x, test_x, train_y, test_y = train_test_split(data2, label, test_size=0.5, random_state=0) xgb_train = xgb.DMatrix(train_x, label=train_y) xgb_test = xgb.DMatrix(test_x, label=test_y) params = { 'eval_metric': 'rmse', 'max_depth': int(max_depth), 'learning_rate': learning_rate, 'n_estimators': int(n_estimators), 'gamma': gamma, 'min_child_weight': int(min_child_weight), 'subsample': subsample, 'colsample_bytree': colsample_bytree, 'n_jobs': -1, 'random_state': 42 } # 进行交叉验证 cv_result = xgb.cv(params, xgb_train, num_boost_round=100, early_stopping_rounds=10, stratified=False) return -1.0 * cv_result['test-rmse-mean'].iloc[-1] # 定义参数范围 pbounds = {'max_depth': (3, 10), 'learning_rate': (0.01, 0.3), 'n_estimators': (50, 200), 'gamma': (0, 10), 'min_child_weight': (1, 10), 'subsample': (0.5, 1), 'colsample_bytree': (0.1, 1)} # 进行贝叶斯优化，找到最优超参数 optimizer = BayesianOptimization(f=xgb_cv, pbounds=pbounds, random_state=42) optimizer.maximize(init_points=5, n_iter=25) # 输出最优结果 print(optimizer.max) model = xgb.train(optimizer.max, xgb_train) model.save_model("model3.xgb") return optimizer.max

params1 = xgb_cv(max_depth=5, learning_rate=0.1, n_estimators=100, gamma=0.1, min_child_weight=1, subsample=0.8, colsample_bytree=0.8) 其中，你可以根据你的具体需求，设置这些参数的值，以得到最佳的...

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