from sklearn.linear_model import LogisticRegression # 定义逻辑回归模型 model = LogisticRegression(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’liblinear’, max_iter=100, multi_class=’ovr’, verbose=0...
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) 训练模型 sklearn库提供了多种机器学习模型,如线性回归、支持向量机、决策树等。以下是使用线性回归模型的示例: from sklearn.linear_model import Li...
from sklearn.linear_model import LogisticRegression 1.1.2 版本 scikit-learn==0.21.3 1.1.3 参数 1.1.3.1 penalty l1、l2、elasticnet、none,默认l2 l1: l1正则,邻回归 l2: l2正则,套索回归 elasticnet: 弹性网络,是邻回归和套索回归的正则项的混合 none: 什么都不加 在调参时如果我们主要的目的只是为了...
model_selection import train_test_split from sklearn.linear_model import LinearRegression import numpy as np # 创建一些示例数据 X = np.array([[1], [2], [3], [4], [5]]) y = np.array([2, 4, 6, 8, 10]) # 将数据分为训练集和测试集 X_train, X_test, y_train, y_test = ...
# 导入所需的库和模块importnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.linear_modelimportLinearRegressionfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportmean_squared_error, r2_score# 准备数据集X = np.array([1,2,3,4,5]).reshape(-1,1) ...
```python from sklearn.linear_model import LogisticRegression import numpy as np import matplotlib....
from sklearn.linear_model import LogisticRegression clf = LogisticRegression(penalty='l2', --惩罚项(l1与l2),默认l2 dual=False, --对偶或原始方法,默认False,样本数量>样本特征的时候,dual通常设置为False tol=0.0001, --停止求解的标准,float类型,默认为1e-4。就是求解到多少的时候,停止,认为已经求出最...
1fromsklearnimportmetrics2fromsklearn.linear_modelimportLogisticRegression3model = LogisticRegression()4model.fit(X, y)5print('MODEL')6print(model)7# make predictions8expected = y9predicted = model.predict(X)10# summarize the fit of the model11print('RESULT')12print(metrics.classification_report...
linear_model import LinearRegression # 创建模型 model = LinearRegression() # 训练模型 model.fit(X_train, y_train) 4. 预测与评估 接下来,我们使用训练好的模型进行预测,并评估预测结果的准确性。 # 预测测试集 y_pred = model.predict(X_test) # 评估预测结果 mse = ((y_test - y_pred) ** 2...
importpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportLabelEncoderfromsklearn.preprocessingimportStandardScalerfromsklearn.decompositionimportPCAfromsklearn.linear_modelimportLogisticRegressionfromsklearn.pipelineimportPipeline#需要联网df = pd.read_csv('http://archive.ics.uci...