商品分类实验,数据集构建AdaBoost分类器，并对模型进行评估。深入理解集成学习的原理，能够使用该算法完成预定的任务。,读入数据，并查看数据分布情况，将数据集进行划分：70%训练、30%验证代码

好的，以下是Python代码示例：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score

# 读取数据集
data = pd.read_csv('data.csv')

# 查看数据分布情况
print(data['category'].value_counts())

# 划分数据集
train_data, valid_data = train_test_split(data, test_size=0.3, random_state=42)

# 特征工程
# 这里使用词袋模型作为特征
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
train_features = vectorizer.fit_transform(train_data['name'])
valid_features = vectorizer.transform(valid_data['name'])

# 模型构建
model = AdaBoostClassifier(n_estimators=50, learning_rate=0.1, random_state=42)
model.fit(train_features, train_data['category'])

# 模型评估
train_pred = model.predict(train_features)
train_acc = accuracy_score(train_data['category'], train_pred)
train_precision = precision_score(train_data['category'], train_pred, average='macro')
train_recall = recall_score(train_data['category'], train_pred, average='macro')
print('训练集：准确率=%.2f，精确率=%.2f，召回率=%.2f' % (train_acc, train_precision, train_recall))

valid_pred = model.predict(valid_features)
valid_acc = accuracy_score(valid_data['category'], valid_pred)
valid_precision = precision_score(valid_data['category'], valid_pred, average='macro')
valid_recall = recall_score(valid_data['category'], valid_pred, average='macro')
print('验证集：准确率=%.2f，精确率=%.2f，召回率=%.2f' % (valid_acc, valid_precision, valid_recall))

注意：以上代码只是一个示例，具体实现可能需要根据数据集的特点进行调整。