x_train = train.drop(['id','label'], axis=1) y_train = train['label'] x_test=test.drop(['id'], axis=1) def abs_sum(y_pre,y_tru): y_pre=np.array(y_pre) y_tru=np.array(y_tru) loss=sum(sum(abs(y_pre-y_tru))) return loss def cv_model(clf, train_x, train_y, test_x, clf_name): folds = 5 seed = 2021 kf = KFold(n_splits=folds, shuffle=True, random_state=seed) test = np.zeros((test_x.shape[0],4)) cv_scores = [] onehot_encoder = OneHotEncoder(sparse=False) for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)): print('************************************ {} ************************************'.format(str(i+1))) trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index] if clf_name == "lgb": train_matrix = clf.Dataset(trn_x, label=trn_y) valid_matrix = clf.Dataset(val_x, label=val_y) params = { 'boosting_type': 'gbdt', 'objective': 'multiclass', 'num_class': 4, 'num_leaves': 2 ** 5, 'feature_fraction': 0.8, 'bagging_fraction': 0.8, 'bagging_freq': 4, 'learning_rate': 0.1, 'seed': seed, 'nthread': 28, 'n_jobs':24, 'verbose': -1, } model = clf.train(params, train_set=train_matrix, valid_sets=valid_matrix, num_boost_round=2000, verbose_eval=100, early_stopping_rounds=200) val_pred = model.predict(val_x, num_iteration=model.best_iteration) test_pred = model.predict(test_x, num_iteration=model.best_iteration) val_y=np.array(val_y).reshape(-1, 1) val_y = onehot_encoder.fit_transform(val_y) print('预测的概率矩阵为：') print(test_pred) test += test_pred score=abs_sum(val_y, val_pred) cv_scores.append(score) print(cv_scores) print("%s_scotrainre_list:" % clf_name, cv_scores) print("%s_score_mean:" % clf_name, np.mean(cv_scores)) print("%s_score_std:" % clf_name, np.std(cv_scores)) test=test/kf.n_splits return test def lgb_model(x_train, y_train, x_test): lgb_test = cv_model(lgb, x_train, y_train, x_test, "lgb") return lgb_test lgb_test = lgb_model(x_train, y_train, x_test) 这段代码运用了什么学习模型

这段代码运用了LightGBM模型（lgb）进行多分类任务的学习和预测。其中，使用了K折交叉验证（KFold）来划分训练集和验证集，避免过拟合和欠拟合。在训练过程中，使用了绝对误差和（abs_sum）作为损失函数。在LightGBM模型的参数设置上，使用了gbdt算法进行梯度提升决策树，num_class参数设置为4，表示有4个类别；num_leaves参数设置为2的5次方，表示叶节点的数量；feature_fraction和bagging_fraction是特征和样本的子抽样比例；learning_rate是学习率；early_stopping_rounds设置为200，表示在验证集上连续200次迭代中没有提高时，停止训练；n_jobs和nthread是并行训练的参数。最终，返回了测试集上的预测结果（lgb_test）。

相关问题

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix, classification_report, accuracy_score # 1. 数据准备 train_data = pd.read_csv('train.csv') test_data = pd.read_csv('test_noLabel.csv') # 填充缺失值 train_data.fillna(train_data.mean(), inplace=True) test_data.fillna(test_data.mean(), inplace=True) # 2. 特征工程 X_train = train_data.drop(['Label', 'ID'], axis=1) y_train = train_data['Label'] X_test = test_data.drop('ID', axis=1) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 3. 模型建立 model = RandomForestClassifier(n_estimators=100, random_state=42) # 4. 模型训练 model.fit(X_train, y_train) # 5. 进行预测 y_pred = model.predict(X_test) # 6. 保存预测结果 df_result = pd.DataFrame({'ID': test_data['ID'], 'Label': y_pred}) df_result.to_csv('forecast_result.csv', index=False) # 7. 模型评估 y_train_pred = model.predict(X_train) print('训练集准确率：', accuracy_score(y_train, y_train_pred)) print('测试集准确率：', accuracy_score(y_test, y_pred)) print(classification_report(y_test, y_pred)) # 8. 绘制柱形图 feature_importances = pd.Series(model.feature_importances_, index=X_train.columns) feature_importances = feature_importances.sort_values(ascending=False) plt.figure(figsize=(10, 6)) sns.barplot(x=feature_importances, y=feature_importances.index) plt.xlabel('Feature Importance Score') plt.ylabel('Features') plt.title('Visualizing Important Features') plt.show() # 9. 对比类分析 train_data['Label'].value_counts().plot(kind='bar', color=['blue', 'red']) plt.title('Class Distribution') plt.xlabel('Class') plt.ylabel('Frequency') plt.show()

这段代码是一个机器学习模型的完整流程，包括数据准备、特征工程、模型建立、模型训练、预测结果保存、模型评估和可视化分析等步骤。其中包括了绘制柱形图和对比类分析的代码。

绘制柱形图的代码如下：

feature_importances = pd.Series(model.feature_importances_, index=X_train.columns)
feature_importances = feature_importances.sort_values(ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x=feature_importances, y=feature_importances.index)
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title('Visualizing Important Features')
plt.show()

这段代码会生成一个柱形图，横轴为特征重要性得分，纵轴为特征名称，用于展示机器学习模型中各个特征的重要性。

对比类分析的代码如下：

train_data['Label'].value_counts().plot(kind='bar', color=['blue', 'red'])
plt.title('Class Distribution')
plt.xlabel('Class')
plt.ylabel('Frequency')
plt.show()

这段代码会生成一个柱形图，展示训练数据中不同类别的样本数量分布情况。这个图可以用于判断数据集是否存在类别不平衡的情况，以及对模型的训练和评估结果进行解释。

import pandas as pd import numpy as np from sklearn.model_selection import StratifiedKFold, KFold from sklearn.svm import SVR train = pd.read_csv('train.csv') test = pd.read_csv('test.csv') # 分离数据集 X_train_c = train.drop(['ID', 'CLASS'], axis=1).values y_train_c = train['CLASS'].values X_test_c = test.drop(['ID'], axis=1).values nfold = 5 kf = KFold(n_splits=nfold, shuffle=True, random_state=2020) prediction1 = np.zeros((len(X_test_c),)) i = 0 for train_index, valid_index in kf.split(X_train_c, y_train_c): print("\nFold {}".format(i + 1)) X_train, label_train = X_train_c[train_index], y_train_c[train_index] X_valid, label_valid = X_train_c[valid_index], y_train_c[valid_index] clf = SVR(kernel='rbf', C=1, gamma='scale') clf.fit(X_train, label_train) x1 = clf.predict(X_valid) y1 = clf.predict(X_test_c) prediction1 += y1 / nfold i += 1 result1 = np.round(prediction1) id_ = range(210, 314) df = pd.DataFrame({'ID': id_, 'CLASS': result1}) df.to_csv("baseline.csv", index=False)

这段代码是一个基线模型，使用了支持向量回归（SVR）算法，通过 K 折交叉验证来训练模型，并对测试集进行预测。其中，训练数据集和测试数据集均为 CSV 格式的文件，需要使用 pandas 库读取。具体的实现过程如下：

1. 读取训练集和测试集的数据，并进行预处理。

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

X_train_c = train.drop(['ID', 'CLASS'], axis=1).values
y_train_c = train['CLASS'].values
X_test_c = test.drop(['ID'], axis=1).values

2. 定义 K 折交叉验证器，并进行模型训练和预测。

nfold = 5
kf = KFold(n_splits=nfold, shuffle=True, random_state=2020)
prediction1 = np.zeros((len(X_test_c),))

i = 0
for train_index, valid_index in kf.split(X_train_c, y_train_c):
    print("\nFold {}".format(i + 1))
    X_train, label_train = X_train_c[train_index], y_train_c[train_index]
    X_valid, label_valid = X_train_c[valid_index], y_train_c[valid_index]

    clf = SVR(kernel='rbf', C=1, gamma='scale')
    clf.fit(X_train, label_train)

    x1 = clf.predict(X_valid)
    y1 = clf.predict(X_test_c)

    prediction1 += y1 / nfold
    i += 1

3. 对测试集的预测结果进行处理，并将结果保存到 CSV 文件中。

result1 = np.round(prediction1)
id_ = range(210, 314)
df = pd.DataFrame({'ID': id_, 'CLASS': result1})
df.to_csv("baseline.csv", index=False)

其中，`prediction1` 是对测试集的预测结果进行累加的数组，`result1` 是将累加结果四舍五入后得到的最终预测结果。最后将结果保存到 CSV 文件中，方便后续的提交。