网址：https://www.kaggle.com/code/dansbecker/model-validation
代码
1.What is Model Validation
就是衡量模型的预测能力，一般用预测值和现实值，这两的差距是多少来衡量。沿用这个原理，提出一个概念Mean Absolute Error (also called MAE). 通俗易懂地说就是“平均而言，我们的预测偏差是多少”，以下是计算MAE的代码：
from sklearn.metrics import mean_absolute_errorpredicted_home_prices = melbourne_model.predict(X)
mean_absolute_error(y, predicted_home_prices)
输出MAE：
434.71594577146544
2.The Problem with “In-Sample” Scores
这一小段结论是：将之前的训练集数据一分为二，一部分用于训练模型，另一部分用于检验模型预测性能。
原因是：如果用训练集数据去检验模型预测性能，显而易见，结果会非常好，但不准确。
3.Coding It
1.使用train_test_split，将数据分成训练集train_X、train_y, 验证集val_X、val_y 。
2.计算MAE
from sklearn.model_selection import train_test_split# split data into training and validation data, for both features and target
# The split is based on a random number generator. Supplying a numeric value to
# the random_state argument guarantees we get the same split every time we
# run this script.
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)
# Define model
melbourne_model = DecisionTreeRegressor()
# Fit model
melbourne_model.fit(train_X, train_y)# get predicted prices on validation data
val_predictions = melbourne_model.predict(val_X)
print(mean_absolute_error(val_y, val_predictions))
输出MAE：
265806.91478373145
4.Wow!
真没想到，预测偏差这么大，没分数据前是500刀，分了数据后是26万刀。要学习下怎样优化模型。
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