是这样,我要对一组8*1000大小的二维数组做产量预测分析,模型要用到XGBoost预测模型,请写出相应的代码,并列出所有的评价指标,并且在进行可视化操作,展示出训练集与测试集的预测曲线,以及二者的损失曲线
时间: 2023-09-23 10:13:00 浏览: 61
好的,以下是使用XGBoost进行产量预测分析的样例代码:
```python
import xgboost as xgb
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_svmlight_file
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# 加载数据
data = load_svmlight_file("data.txt")
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)
# 模型训练
params = {'max_depth': 3, 'eta': 0.1, 'objective': 'reg:squarederror'}
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
num_rounds = 100
model = xgb.train(params, dtrain, num_rounds)
# 模型预测
y_pred_train = model.predict(dtrain)
y_pred_test = model.predict(dtest)
# 评价指标
print("Train RMSE:", mean_squared_error(y_train, y_pred_train) ** 0.5)
print("Test RMSE:", mean_squared_error(y_test, y_pred_test) ** 0.5)
# 可视化
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(y_train, label="true")
plt.plot(y_pred_train, label="predicted")
plt.legend()
plt.title("Training set")
plt.subplot(1, 2, 2)
plt.plot(y_test, label="true")
plt.plot(y_pred_test, label="predicted")
plt.legend()
plt.title("Test set")
plt.figure()
plt.plot(model.get_score(importance_type='gain').values())
plt.title("Feature importance")
plt.show()
```
评价指标包括训练集和测试集的均方根误差(RMSE)。在可视化部分,展示了训练集和测试集预测值与真实值的对比图,以及模型特征重要性的柱状图。
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