分析此段代码功能： from sklearn.datasets import load_boston boston=load_boston() boston.data.shape

这段代码的功能是导入scikit-learn库中的load_boston方法，用于加载波士顿房价数据集，并将数据集存储在变量boston中。最后，该代码返回数据集的维度，即数据集包含的样本数和特征数。其中，boston.data.shape用于返回数据集的维度信息，也就是该数据集包含的样本数和特征数。

相关问题

1. 读入数据sklearn中的波士顿房价的数据集。 from sklearn.datasets import load_boston 2. 对该数据集进行可视化 3. 使用GBDT对模型进行预测，要求使用交叉验证训练，给出MSE误差曲线。

好的，我来为您解答这个问题。

首先，我们需要导入相关的库和数据集：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# 加载波士顿房价数据集
boston = load_boston()
X, y = boston.data, boston.target

接着，我们可以对数据集进行可视化，以了解数据的分布情况：

# 可视化数据集
fig, axes = plt.subplots(nrows=4, ncols=4, sharey=True, figsize=(15, 12))
for i in range(4):
    for j in range(4):
        col = i * 4 + j
        if col < X.shape[1]:
            axes[i, j].scatter(X[:, col], y, s=5)
            axes[i, j].set_xlabel(boston.feature_names[col])
axes[0, 0].set_ylabel('Price')
plt.show()

接下来，我们使用GBDT对数据集进行建模，并进行交叉验证：

# 使用GBDT进行建模
gbdt = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42)

# 进行交叉验证，并计算MSE误差
train_error, test_error = [], []
for i in range(1, 100, 5):
    gbdt.n_estimators = i
    gbdt.fit(X_train, y_train)
    train_error.append(mean_squared_error(y_train, gbdt.predict(X_train)))
    test_error.append(mean_squared_error(y_test, gbdt.predict(X_test)))

# 可视化MSE误差曲线
plt.plot(range(1, 100, 5), train_error, 'o-', color='r', label='Training Error')
plt.plot(range(1, 100, 5), test_error, 'o-', color='g', label='Testing Error')
plt.legend(loc='best')
plt.xlabel('Number of Estimators')
plt.ylabel('MSE')
plt.show()

最后，我们可以得到GBDT模型的MSE误差曲线，以及模型的预测结果。

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()

这段代码使用TensorFlow和Keras实现了一个神经网络模型，用于预测波士顿房价。以下是代码的解释：

首先，导入必要的库和模块：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle
from sklearn.preprocessing import scale

然后，读取波士顿房价数据集并对其进行预处理：

df = pd.read_csv("C:\\boston.csv",header=0)
ds = df.values

接着，从sklearn.datasets模块中加载波士顿房价数据集，并将其分为训练集和测试集：

from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

对训练集和测试集进行标准化处理：

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

定义一个包含4个Dense层的神经网络模型：

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(64, activation='relu'),
    Dense(1)
])

编译模型并训练：

model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32)

使用模型对测试集进行预测，并计算均方误差：

from sklearn.metrics import mean_squared_error
y_pred = model.predict(x_test)
mse = mean_squared_error(y_test, y_pred)
print('MSE:’, mse)

最后，绘制模型的训练和验证准确率曲线：

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='validation')

plt.legend()

plt.show()