AdaBoosting示例

使用AdaBoosting预测波士顿房价

决策树示例：波士顿房价预测

根据13个特征预测房价价格

import sklearn.datasets as sd
import sklearn.utils as su
import sklearn.tree as st
import sklearn.metrics as sm
import sklearn.ensemble as se

读取数据集

boston = sd.load_boston()
# for sample in boston.data:
#     print(sample)

样本随机化(打乱)

random_seed = 7  # 随机种子，用来产生随机数
x, y = su.shuffle(boston.data,
                  boston.target,
                  random_state=random_seed)

分训练集、测试集

train_size = int(len(x) * 0.8)  # 计算训练集大小(80%)

train_x = x[:train_size]  # 切出前面80%
test_x = x[train_size:]  # 切出后面20%

train_y = y[:train_size]  # 切出前面80%
test_y = y[train_size:]  # 切出后面20%

model = se.AdaBoostRegressor(
    st.DecisionTreeRegressor(max_depth=4),  # 原始
    n_estimators=400)  # 数棵树
model.fit(train_x, train_y)  # 训练
pred_test_y = model.predict(test_x)  # 预测

计算并打印R2值

print(sm.r2_score(test_y, pred_test_y))

随机森林

model2 = se.RandomForestRegressor(
    max_depth=10,  # 最大深度
    n_estimators=1000,  # 树数量
    min_samples_split=2)  # 最小样本数量
model2.fit(train_x, train_y)
pred_test_y = model2.predict(test_x)  # 预测

计算并打印R2值

print(sm.r2_score(test_y, pred_test_y))

标签：sklearn,房价,boston,AdaBoosting,train,test,import,波士顿,size
来源： https://blog.csdn.net/weixin_49304690/article/details/112626947