机器学习模型中的欠拟合与过拟合分析

Question

下面的快照显示了我的代码，以获得mse和我的模型在培训和测试期间的分数。根据守则，是否可以假定：

1. 看看RandomForestRegressor，它真的表明模型在训练集上表现不佳吗？因为MSE在训练集上是高的，在测试集上是低的。我们可以说模特不合身吗？

同样的，

1. XGBRegressor，我有低的训练误差和高的测试误差。这是否意味着，这个模型太合适了？

快照

Answer 1

RF和XGB回归器都有过度拟合的问题。使用交叉验证解决此问题。例如,

from sklearn.datasets import make_regression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline

X, y = make_regression(n_samples=100)

from sklearn.model_selection import GridSearchCV
# Create the parameter grid based on the results of random search 
param_grid = {
    'bootstrap': [True],
    'max_depth': [80, 90, 100, 110],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'min_samples_split': [8, 10, 12],
    'n_estimators': [100, 200, 300, 1000]
}
# Create a based model
rf = RandomForestRegressor()
# Instantiate the grid search model
grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, 
                          cv = 3, n_jobs = -1, verbose = 2)
# Fit the grid search to the data
grid_search.fit(X, y)
grid_search.best_params_