Example
There are a few alternatives for controlling the tree depth, and many allow for some routes through the tree to have greater depth than other routes. But the max_leaf_nodes argument provides a very sensible way to control overfitting vs underfitting. The more leaves we allow the model to make, the more we move from the underfitting area in the above graph to the overfitting area.
通過for循環(huán)實(shí)現(xiàn)通過平均絕對誤差函數(shù)進(jìn)行最優(yōu)參數(shù)的選擇
We can use a utility function to help compare MAE scores from different values for max_leaf_nodes:
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(train_X, train_y)
preds_val = model.predict(val_X)
mae = mean_absolute_error(val_y, preds_val)
return(mae)
The data is loaded into train_X, val_X, train_y and val_y using the code you've already seen (and which you've already written).
We can use a for-loop to compare the accuracy of models built with different values for max_leaf_nodes.
# compare MAE with differing values of max_leaf_nodes
for max_leaf_nodes in [5, 50, 500, 5000]:
my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)
print("Max leaf nodes: %d \t\t Mean Absolute Error: %d" %(max_leaf_nodes, my_mae))
Max leaf nodes: 5 Mean Absolute Error: 347380
Max leaf nodes: 50 Mean Absolute Error: 258171
Max leaf nodes: 500 Mean Absolute Error: 243495
Max leaf nodes: 5000 Mean Absolute Error: 254983
