使用LASSON 数据处理 python案例

以下是使用LASSO进行数据处理的Python案例：

首先，我们需要导入所需的库和数据集。在本例中，我们将使用sklearn库中的Boston Housing数据集。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

boston_data = load_boston()
X = pd.DataFrame(boston_data.data, columns=boston_data.feature_names)
y = pd.Series(boston_data.target)

接下来，我们将对数据进行标准化处理，并将其分成训练集和测试集。

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

然后，我们将使用LASSO模型进行拟合，并计算其在测试集上的R²得分。

lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)

train_score = lasso.score(X_train, y_train)
test_score = lasso.score(X_test, y_test)

print(f"Train score: {train_score:.3f}")
print(f"Test score: {test_score:.3f}")

最后，我们可以通过查看LASSO模型的系数来确定哪些特征对目标变量有重要影响。

features = pd.DataFrame({'Feature': X.columns, 'Coefficient': lasso.coef_})
important_features = features[features['Coefficient'] != 0]
print(important_features)

完整代码如下：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

boston_data = load_boston()
X = pd.DataFrame(boston_data.data, columns=boston_data.feature_names)
y = pd.Series(boston_data.target)

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)

train_score = lasso.score(X_train, y_train)
test_score = lasso.score(X_test, y_test)

print(f"Train score: {train_score:.3f}")
print(f"Test score: {test_score:.3f}")

features = pd.DataFrame({'Feature': X.columns, 'Coefficient': lasso.coef_})
important_features = features[features['Coefficient'] != 0]
print(important_features)