所以我需要建立一个回归模型，基于11个输入来预测葡萄酒的质量。目前我正在评估各种算法的均方误差、平均绝对误差和R2分数。我想做出选择哪个算法使用的决定，但在此之前，我想确保我的数据没有过度拟合/欠拟合。以下是我使用的数据集的链接（它有点不同，但数据完全相同），以及我的整个代码。
非常感谢任何帮助！
非常感谢任何帮助！
Data: https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/
此外，我从以下 Kaggle 链接中复制了大部分代码： https://www.kaggle.com/jhansia/regression-models-analysis-on-the-wine-quality
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
wine = pd.read_csv('wineQualityReds.csv', usecols=lambda x: 'Unnamed' not in x,)
wine.head()
y = wine.quality
X = wine.drop('quality',axis = 1)
from sklearn.model_selection import train_test_split
train_x,test_x,train_y,test_y = train_test_split(X,y,random_state = 0, stratify = y)
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(train_x)
train_x_scaled = scaler.transform(train_x)
test_x_scaled = scaler.transform(test_x)
from sklearn import model_selection
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm import SVR
from sklearn.metrics import mean_absolute_error
models = []
models.append(('DecisionTree', DecisionTreeRegressor()))
models.append(('RandomForest', RandomForestRegressor()))
models.append(('GradienBoost', GradientBoostingRegressor()))
models.append(('SVR', SVR()))
names = []
for name,model in models:
kfold = model_selection.KFold(n_splits=5,random_state=2)
cv_results = model_selection.cross_val_score(model,train_x_scaled,train_y, cv= kfold, scoring = 'neg_mean_absolute_error')
names.append(name)
msg = "%s: %f" % (name, -1*(cv_results).mean())
print(msg)
model = RandomForestRegressor()
model.fit(train_x_scaled,train_y)
pred_y = model.predict(test_x_scaled)
from sklearn import metrics
print('Mean Squared Error:', metrics.mean_squared_error(test_y, pred_y))
print('Mean Absolute Error:', metrics.mean_absolute_error(test_y, pred_y))
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(test_y, pred_y)))
print('R2:', metrics.r2_score(test_y, pred_y))