如何从新的数据集进行预测？

# 示例代码：特征工程
import pandas as pd

# 假设训练数据集和新数据集分别为train_data和new_data
train_data = pd.read_csv('train_data.csv')
new_data = pd.read_csv('new_data.csv')

# 特征提取和转换
train_features = train_data[['feature1', 'feature2']]
new_features = new_data[['feature1', 'feature2']]

# 归一化
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
train_features_scaled = scaler.fit_transform(train_features)
new_features_scaled = scaler.transform(new_features)

# 示例代码：正则化
from sklearn.linear_model import Ridge

# 使用Ridge回归进行正则化
ridge_model = Ridge(alpha=0.5)
ridge_model.fit(train_features_scaled, train_labels)

# 示例代码：增加训练轮数
from sklearn.ensemble import RandomForestClassifier

# 使用随机森林分类器
rf_model = RandomForestClassifier(n_estimators=100, max_depth=10)
rf_model.fit(train_features_scaled, train_labels)