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在python中实现预测
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# 读取数据
df = pd.read_csv("data.csv")
# 划分数据为训练集和预测集
train_df, predict_df = train_test_split(df, test_size=0.2, random_state=0)
# 分离训练集的特征和标签
train_x = train_df.drop("target", axis=1)
train_y = train_df["target"]
# 训练模型
model = RandomForestRegressor(n_estimators=100, random_state=0)
model.fit(train_x, train_y)
# 利用训练好的模型预测
predict_x = predict_df.drop("target", axis=1)
predict_y = predict_df["target"]
predictions = model.predict(predict_x)
# 计算预测误差
mse = mean_squared_error(predict_y, predictions)
print("预测误差:", mse)
如果您想使用模型预测预测数据,可以在上面的代码中添加以下代码:
# 读取预测数据
pred_data = pd.read_csv("pred_data.csv")
# 预测
predictions = model.predict(pred_data)