xgb_classifier = XGBClassifier() label_encoder = LabelEncoder() label_encoder.fit(train_labels) train_labels_encoded = label_encoder.transform(train_labels) test_labels_encoded = label_encoder.transform(test_labels) xgb_classifier.fit(feature_matrix, train_labels_encoded) xgb_pred = xgb_classifier.predict(test_tfidf) xgb_acc = accuracy(labels_true=test_labels, labels_pred=xgb_pred) micro_F1 = f1_score(test_labels_encoded, xgb_pred, average='micro') macro_f1 = f1_score(test_labels_encoded, xgb_pred, average='macro') print("XGBoost分类准确率: %.4f, micro_F1: %.4f, macro_f1值: %.4f" % (xgb_acc, micro_F1, macro_f1))代码翻译

分析这些代码；model_XGB = XGBClassifier() eval_set = [(val_X, val_y)] model_XGB.fit(train_X, train_y, early_stopping_rounds=500, eval_metric="logloss", eval_set=eval_set, verbose=False) y_pred = model_XGB.predict(val_X)

这段代码是采用 XGBoost 分类算法建立了一个分类器模型，通过 fit 方法在训练集上拟合该模型，并使用 early_stopping_rounds 对模型训练过程进行提前停止，以防止过拟合。eval_set 存放的是用于验证模型的数据集，...

new_data = new_data.drop('event_type', axis=1) # 划分数据集 X = new_data.drop(["user_id","product_id","new_column"], axis=1) y = new_data["new_column"] # 将数据集划分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) le = LabelEncoder() y_train = le.fit_transform(y_train) import xgboost as xgb from sklearn.metrics import accuracy_score # 定义xgboost模型 xgb_model = xgb.XGBClassifier() # 设置参数空间 best_params= { 'max_depth': 5, 'learning_rate': 0.05, 'n_estimators': 200, 'min_child_weight': 1.5, 'subsample':0.7, 'reg_alpha': 0.1, 'reg_lambda': 0.1, } # 输出最优参数 print('Best Parameter:', best_params) # 使用最优参数构建新的xgboost模型 best_xgb_model = xgb.XGBClassifier(**best_params) best_xgb_model.fit(X_train, y_train) # 使用新模型进行预测 y_pred = best_xgb_model.predict(X_test)改为使用svm模型

好的，将xgboost模型改为svm模型，代码如下： from sklearn.svm import SVC from sklearn.metrics ...best_svm_model.fit(X_train, y_train) # 使用新模型进行预测 y_pred = best_svm_model.predict(X_test)

xgb_model = XGBClassifier(params) TypeError: 'module' object is not callable

这个错误通常是由于导入的模块与变量名冲突导致的。根据错误提示，可能是因为你导入的模块名与变量名相同，导致无法调用模块...在这个例子中，我们将xgboost模块导入为xgb，并使用xgb.XGBClassifier来创建分类器对象。

import xgboost as xgbxgb_model = xgb.XGBClassifier()xgb_model.fit(train_data, train_label)，这个里面怎么填写

您需要将train_data替换为您的训练数据，train_label替换为您的训练...xgb_model = xgb.XGBClassifier() xgb_model.fit(train_data, train_label) 请注意，这只是一个示例。您需要根据您的数据集进行适当的更改。

xgb_train = xgb.DMatrix(train_x, label=train_y)

这行代码使用xgboost库的DMatrix函数将训练数据train_x和train_y转换为xgb_train，即xgboost模型所需的数据格式。其中train_x是训练集的特征矩阵，train_y是训练集的标签。在xgboost中，DMatrix是一种数据格式，可以...

xgb_reg = XGBRegressor(**xgb_params) xgb_reg.fit(x_train, y_train, early_stopping_rounds=10, eval_set=[(x_val, y_val)], verbose=False) val_loss = xgb_reg.evals_result()['validation_0']['rmse'][-1] return val_loss

看起来这是一个使用XGBoost库进行回归训练的函数，函数的输入包括训练数据集x_train和y_train，以及验证数据集x_val和y_val。函数中使用了XGBRegressor类初始化模型，通过指定超参数xgb_params来进行训练，并使用...

select count(*) FROM xgb_order_products op LEFT JOIN xgb_orders o ON o.order_id=op.order_id left join xgb_order_after_sales oas on op.order_id = oas.order_id where (op.all_refund = 0 or (oas.type = 2 and oas.status = 5) and o.status = 4 and op.is_comment = 0 and o.user_id = 492740 and op.is_gift = 0

- 订单商品表 xgb_order_products 中的 all_refund 字段等于 0； - 订单表 xgb_orders 中的 status 等于 4，user_id 等于 492740； - 订单商品表 xgb_order_products 中的 is_comment 字段等于 0，is_gift 字段等于 ...

from sklearn.utils.class_weight import compute_sample_weight sample_weights = compute_sample_weight( class_weight='balanced',y=train_df['class'] #provide your own target name ) xgb_classifier.fit(X,y,sample_weight=sample_weights）是什么意思，该如何修改

这段代码使用了 scikit-learn 中的 compute_sample_weight 函数来计算样本权重，以便在...同时，如果你不想使用样本权重，也可以将最后一行的 sample_weight 参数去掉，直接使用 xgb_classifier.fit(X, y) 来拟合数据。

vectorizer = TfidfVectorizer() twitter_train_X = vectorizer.fit_transform(twitter_train_df['text']) twitter_test_X = vectorizer.transform(twitter_test_df['text']) twitter_train_y = twitter_train_df['sentiment'] twitter_test_y = twitter_test_df['sentiment'] # 构建 XGBoost 模型并训练 dtrain = xgb.DMatrix(twitter_train_X, label=twitter_train_y) dtest = xgb.DMatrix(twitter_test_X, label=twitter_test_y) param = {'max_depth': 3, 'eta': 0.1, 'objective': 'multi:softmax', 'num_class': 3} num_round = 100 bst = xgb.train(param, dtrain, num_round)可以帮我把这段代码换成卷积神经网络吗

history = model.fit(X_train, y_train, batch_size=batch_size, epochs=num_epochs, validation_data=(X_test, y_test)) 其中，vocab_size 表示词汇表大小，embedding_size 表示词向量维度，max_length ...

分析这些代码，并且解释每个函数的作用：scores_XGB = [] scores_XGB.append(precision_score(val_y, y_pred)) scores_XGB.append(recall_score(val_y, y_pred)) confusion_matrix_XGB = confusion_matrix(val_y,y_pred) f1_score_XGB = f1_score(val_y, y_pred,labels=None, pos_label=0, average="binary", sample_weight=None) predictions_xgb = model_XGB.predict_proba(val_X) # 每一类的概率 FPR_xgb, recall_xgb, thresholds = roc_curve(val_y,predictions_xgb[:,1], pos_label=1) area_xgb = auc(FPR_xgb,recall_xgb)

5. f1_score_XGB = f1_score(val_y, y_pred,labels=None, pos_label=0, average="binary", sample_weight=None)：计算F1得分并将其分配给f1_score_XGB变量，使用真实标签val_y和预测标签y_pred，具有二元...

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

接下来，它将使用 xgboost 提供的 xgb.cv() 函数进行交叉验证，并返回最优模型的 rmse 值。然后，它定义了超参数的范围，并使用贝叶斯优化算法寻找最优超参数。最后，它训练了一个 xgboost 模型，并将其保存到文件...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from termcolor import colored as cl import itertools from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import VotingClassifier # 定义模型评估函数 def evaluate_model(y_true, y_pred): accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, pos_label='Good') recall = recall_score(y_true, y_pred, pos_label='Good') f1 = f1_score(y_true, y_pred, pos_label='Good') print("准确率:", accuracy) print("精确率:", precision) print("召回率:", recall) print("F1 分数:", f1) # 读取数据集 data = pd.read_csv('F:\数据\大学\专业课\模式识别\大作业\数据集1\data clean Terklasifikasi baru 22 juli 2015 all.csv', skiprows=16, header=None) # 检查数据集 print(data.head()) # 划分特征向量和标签 X = data.iloc[:, :-1] y = data.iloc[:, -1] # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 6. XGBoost xgb = XGBClassifier(max_depth=4) y_test = np.array(y_test, dtype=int) xgb.fit(X_train, y_train) xgb_pred = xgb.predict(X_test) print("\nXGBoost评估结果：") evaluate_model(y_test, xgb_pred)

6. 使用XGBoost算法进行分类：使用XGBClassifier类构建XGBoost分类器，并使用fit()函数将训练集拟合到该分类器中。然后，使用predict()函数对测试集进行预测，并将预测结果存储在xgb_pred中。 7. 对模型进行评估：...

X_train, X_test, y_train, y_test = train_test_split(X, TC, test_size=0.4,random_state=1) rfc = xgb.XGBRegressor(max_depth=18,learning_rate=0.25,gammma=0) rfc.fit(X_train, y_train) predict_results=rfc.predict(X_test)

然后，创建 xgb.XGBRegressor 对象，并设置 max_depth 参数为 18，learning_rate 参数为 0.25，gamma 参数为 0。这些参数是 XGBoost 模型中的重要参数，它们可以影响模型的性能和准确度。接下来，使用 fit ...

traindata = df1trainlabel = df2Model1 = xgb.XGBRegressor(max_depth=10, learning_rate=0.15, n_estimators=150)Model1.fit(traindata, trainlabel)feature_importance = Model1.feature_importances_.tolist()feature_name = traindata.columns.tolist()

代码中的变量traindata和trainlabel是用于训练模型的数据集，分别表示训练数据和训练标签。Model1是一个XGBRegressor对象，表示构建的回归模型。它使用了max_depth参数来控制树的最大深度，learning_rate参数控制...

from sklearn.model_selection import GridSearchCV print("参数最优化：") xgb_model = xgb.XGBClassifier() param_dict = {'n_estimators': [10,50,100,150], 'learning_rate': [0.01,0.1,0.3]} clf = GridSearchCV(xgb_model, param_dict, verbose=1) clf.fit(X,Y) print(clf.best_score_) print(clf.best_params_)如何把这个代码可视化为热力图

xgb_model = xgb.XGBClassifier() param_dict = {'n_estimators': [10,50,100,150], 'learning_rate': [0.01,0.1,0.3]} clf = GridSearchCV(xgb_model, param_dict, verbose=1) clf.fit(X,Y) results = pd....

model_XGB = XGBClassifier(n_estimators=100, max_depth=3, learning_rate=0.013, n_jobs=-1) model_XGB.fit(X_train, y_train)

xgb_model = xgb.XGBClassifier(learning_rate = 0.1,max_depth = 7,min_child_weight = 5,objective = 'binary:logistic',seed = 0,gamma = 0.1,random_state=0,silent = True) xgb_model.fit(X_train,y_train)

相关推荐

model_XGB = XGBClassifier(n_estimators=100, max_depth=3, learning_rate=0.013, n_jobs=-1) model_XGB.fit(X_train, y_train)

xgb_model = xgb.XGBClassifier(learning_rate = 0.1,max_depth = 7,min_child_weight = 5,objective = 'binary:logistic',seed = 0,gamma = 0.1,random_state=0,silent = True) xgb_model.fit(X_train,y_train)

相关推荐

settings.zip_xgb 网格搜索_xgboost_xgboost格搜索_xgboost网格搜索_网格搜索

Xgboost.zip_XGB_xgboost_xgboost预测_xgb建模

pfm_train_without_pca.csv

分析这些代码；model_XGB = XGBClassifier() eval_set = [(val_X, val_y)] model_XGB.fit(train_X, train_y, early_stopping_rounds=500, eval_metric="logloss", eval_set=eval_set, verbose=False) y_pred = model_XGB.predict(val_X)

xgb_model = XGBClassifier(params) TypeError: 'module' object is not callable

import xgboost as xgbxgb_model = xgb.XGBClassifier()xgb_model.fit(train_data, train_label)，这个里面怎么填写

xgb_train = xgb.DMatrix(train_x, label=train_y)

xgb_reg = XGBRegressor(**xgb_params) xgb_reg.fit(x_train, y_train, early_stopping_rounds=10, eval_set=[(x_val, y_val)], verbose=False) val_loss = xgb_reg.evals_result()['validation_0']['rmse'][-1] return val_loss

from sklearn.utils.class_weight import compute_sample_weight sample_weights = compute_sample_weight( class_weight='balanced',y=train_df['class'] #provide your own target name ) xgb_classifier.fit(X,y,sample_weight=sample_weights）是什么意思，该如何修改

X_train, X_test, y_train, y_test = train_test_split(X, TC, test_size=0.4,random_state=1) rfc = xgb.XGBRegressor(max_depth=18,learning_rate=0.25,gammma=0) rfc.fit(X_train, y_train) predict_results=rfc.predict(X_test)

traindata = df1trainlabel = df2Model1 = xgb.XGBRegressor(max_depth=10, learning_rate=0.15, n_estimators=150)Model1.fit(traindata, trainlabel)feature_importance = Model1.feature_importances_.tolist()feature_name = traindata.columns.tolist()

最新推荐

数据库基础测验20241113.doc

黑板风格计算机毕业答辩PPT模板下载

管理建模和仿真的文件

提升点阵式液晶显示屏效率技术

在SoC芯片的射频测试中，ATE设备通常如何执行系统级测试以保证芯片量产的质量和性能一致？

CodeSandbox实现ListView快速创建指南

"互动学习：行动中的多样性与论文攻读经历"

点阵式显示屏常见故障诊断方法

名词性从句包括哪些类别？它们各自有哪些引导词？请结合例句详细解释。

Node.js脚本实现WXR文件到Postgres数据库帖子导入