基于相亲实验数据，使用sklearn.tree包中决策树算法来绘制决策树，要求如下： 1）criterion参数指定是基尼指数作为样本集分裂的依据绘制决策树，预测验证集数据val_sample = [[24, 178, 2, 17000],[27, 176, 0, 25000],[27, 176, 0, 10000],[32, 170, 2, 42000]]对应的相亲情况；

首先，我们需要读取相亲实验数据，并将其分为训练集和验证集：

import pandas as pd
from sklearn.model_selection import train_test_split

# 读取数据集
df = pd.read_csv('speed_dating.csv', encoding='ISO-8859-1')

# 选择特征
X = df[['age', 'height', 'race', 'income']]
y = df['match']

# 划分训练集和验证集
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

接着，我们可以使用决策树算法来训练模型，并绘制决策树：

from sklearn.tree import DecisionTreeClassifier, export_graphviz
import graphviz

# 创建决策树模型
dtc = DecisionTreeClassifier(criterion='gini', random_state=42)

# 训练模型
dtc.fit(X_train, y_train)

# 绘制决策树
dot_data = export_graphviz(dtc, out_file=None, feature_names=X.columns, class_names=['0', '1'], filled=True, rounded=True, special_characters=True)
graph = graphviz.Source(dot_data)
graph

最后，我们可以使用训练好的模型来预测验证集数据，并输出预测结果：

# 预测验证集数据
val_sample = [[24, 178, 2, 17000],[27, 176, 0, 25000],[27, 176, 0, 10000],[32, 170, 2, 42000]]
y_pred = dtc.predict(val_sample)

# 输出预测结果
for i in range(len(val_sample)):
    print(f"预测{val_sample[i]}的结果为{y_pred[i]}")

完整代码如下：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import graphviz

# 读取数据集
df = pd.read_csv('speed_dating.csv', encoding='ISO-8859-1')

# 选择特征
X = df[['age', 'height', 'race', 'income']]
y = df['match']

# 划分训练集和验证集
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建决策树模型
dtc = DecisionTreeClassifier(criterion='gini', random_state=42)

# 训练模型
dtc.fit(X_train, y_train)

# 绘制决策树
dot_data = export_graphviz(dtc, out_file=None, feature_names=X.columns, class_names=['0', '1'], filled=True, rounded=True, special_characters=True)
graph = graphviz.Source(dot_data)
graph

# 预测验证集数据
val_sample = [[24, 178, 2, 17000],[27, 176, 0, 25000],[27, 176, 0, 10000],[32, 170, 2, 42000]]
y_pred = dtc.predict(val_sample)

# 输出预测结果
for i in range(len(val_sample)):
    print(f"预测{val_sample[i]}的结果为{y_pred[i]}")