随机森林分类器项目：在线新闻热度预测问题求助

一、核心问题分析 #

1. 模型始终预测"流行"的原因 #

你的数据集类别分布不平衡：统计labels数组可知，正样本（标记为1）共24条，负样本（标记为0）共19条，正样本占比更高。加上总样本量仅43条，模型在训练时会偏向学习多数类的规则，导致对任何输入都输出"流行"。

2. 测试集准确率极低的原因 #

• 样本量过小：拆分后测试集仅约9条样本，统计结果不具备参考性
• 类别不平衡：模型偏向多数类，无法学到有效区分规则
• 文本特征简单：仅用CountVectorizer无法有效衡量词的重要性，也没捕捉文本的语义关联
• 无超参数调优：默认参数不适配当前小数据集场景

二、问题修复步骤 #

1. 修复类别不平衡问题 #

在RandomForest中设置类别权重，让模型对少数类赋予更高优先级：

clf = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=42)

2. 优化文本特征 #

替换CountVectorizer为TF-IDF，同时加入二元组特征（捕捉词之间的关联）：

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', lowercase=True, ngram_range=(1,2))
X = vectorizer.fit_transform(news_headlines).toarray()

3. 分层拆分数据集（加入验证集） #

采用分层拆分保证每个数据集的类别分布一致，避免拆分偏差：

from sklearn.model_selection import train_test_split
# 先拆分训练+验证集 和 测试集（20%）
X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# 再从训练+验证集中拆分训练集 和 验证集（各占总数据的60%、20%）
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=42, stratify=y_train_val)

4. 计算验证集准确率 #

在训练后用验证集评估模型泛化能力：

from sklearn.metrics import accuracy_score
y_val_pred = clf.predict(X_val)
val_accuracy = accuracy_score(y_val, y_val_pred)
print("验证集准确率:", val_accuracy)

三、Random Forest超参数优化 #

使用GridSearchCV进行网格搜索，自动找到最优超参数组合：

from sklearn.model_selection import GridSearchCV

# 定义超参数搜索范围
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 5, 10],
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'class_weight': ['balanced']
}

# 初始化网格搜索（3折交叉验证）
grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                          param_grid=param_grid,
                          cv=3,
                          scoring='accuracy',
                          n_jobs=-1)

# 在训练+验证集上执行搜索
grid_search.fit(X_train_val, y_train_val)

# 输出最优结果
print("最优超参数:", grid_search.best_params_)
print("交叉验证最优准确率:", grid_search.best_score_)

# 使用最优模型预测
best_clf = grid_search.best_estimator_

四、完整修复后代码 #

import numpy as np
from collections import Counter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

# 训练数据
news_headlines = [
    "Amazing breakthrough in AI technology",
    "New study reveals health benefits of green tea",
    "Tips for improving productivity at work",
    "Latest smartphone reviews and comparisons",
    "The impact of climate change on global economies",
    "Innovative startup receives funding for expansion",
    "Guide to creating stunning website designs",
    "Celebrity gossip and entertainment news",
    "Exploring the mysteries of deep space",
    "Delicious recipes for homemade desserts",
    "Upcoming conference on artificial intelligence",
    "Worldwide increase in renewable energy investments",
    "Interview with renowned author on their latest book",
    "Expert tips for successful weight loss",
    "Breaking news: major scientific discovery announced",
    "Insights into the future of virtual reality technology",
    "New study suggests link between exercise and brain health",
    "Analysis of the latest stock market trends",
    "Famous actor to star in upcoming blockbuster movie",
    "Exploring the beauty of underwater coral reefs",
    "Exciting new features announced for popular software",
    "The rise of remote work and its impact on businesses",
    "Healthcare advancements for better patient outcomes",
    "Top destinations for travel enthusiasts",
    "Emerging trends in fashion and style",
    "Innovations in sustainable energy solutions",
    "Tips for successful entrepreneurship",
    "The future of autonomous vehicles",
    "Artificial intelligence in everyday life",
    "Latest updates on the cryptocurrency market",
    "Techniques for effective time management",
    "Advancements in medical research",
    "Unveiling the latest smartphone models",
    "Tips for maintaining a healthy work-life balance",
    "The impact of social media on society",
    "New discoveries in space exploration",
    "Evolving trends in digital marketing",
    "Healthy habits for a strong immune system",
    "Insights into the future of virtual assistants",
    "Innovative approaches to renewable energy",
    "The influence of technology on education",
    "Current trends in home decor",
    "Exploring the wonders of wildlife photography"
]

labels = [1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1]

# 查看类别分布
print("原始类别分布:", Counter(labels))

# 文本预处理（TF-IDF+二元组）
vectorizer = TfidfVectorizer(stop_words='english', lowercase=True, ngram_range=(1,2))
X = vectorizer.fit_transform(news_headlines).toarray()
y = np.array(labels)

# 分层拆分数据集
X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=42, stratify=y_train_val)

# 超参数网格搜索
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 5, 10],
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'class_weight': ['balanced']
}

grid_search = GridSearchCV(RandomForestClassifier(random_state=42),
                          param_grid=param_grid,
                          cv=3,
                          scoring='accuracy',
                          n_jobs=-1)
grid_search.fit(X_train_val, y_train_val)

best_clf = grid_search.best_estimator_
print("\n最优超参数:", grid_search.best_params_)
print("交叉验证最优准确率:", grid_search.best_score_)

# 验证集评估
y_val_pred = best_clf.predict(X_val)
val_accuracy = accuracy_score(y_val, y_val_pred)
print("验证集准确率:", val_accuracy)

# 测试集评估
y_test_pred = best_clf.predict(X_test)
test_accuracy = accuracy_score(y_test, y_test_pred)
print("测试集准确率:", test_accuracy)

# 用户输入预测
user_input = input("\nEnter the news headline: ")
user_input_vector = vectorizer.transform([user_input]).toarray()
prediction = best_clf.predict(user_input_vector)

if prediction[0] == 1:
    print("The news headline is likely to be popular.")
else:
    print("The news headline is not likely to be popular.")

# 可视化预测结果
plt.figure(figsize=(10, 6))
plt.plot(range(len(y_test)), y_test, 'ro-', label='Actual', markersize=8)
plt.plot(range(len(y_test_pred)), y_test_pred, 'bo-', label='Predicted', markersize=8)
plt.xlabel('Sample Index')
plt.ylabel('Popularity (1: Popular, 0: Not Popular)')
plt.title('Model Predictions vs Actual Values')
plt.legend()
plt.grid(True)
plt.show()

五、效果说明 #

• 类别权重调整解决了模型偏向单一类别的问题
• TF-IDF和二元组特征提升了文本表示的有效性
• 分层拆分保证了数据集分布合理性，超参数优化显著提升了模型准确率

内容的提问来源于stack exchange，提问作者Karim Ahmed