logistics regression

逻辑回归（logistics regression）是一种广义线性模型，用于预测二元变量的结果（如预测一个人是否罹患某疾病）。它是一种分类算法，通过计算一个样本属于某个类别的概率来决定该样本属于哪个类别。

相关问题

logistics regression python

Sure, I can help you with that! Logistic regression is a popular algorithm for binary classification problems. In Python, you can use the scikit-learn library to implement logistic regression.

Here's an example code snippet for logistic regression in Python:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Assuming you have your data and labels ready
X = ...  # Input features
y = ...  # Binary labels

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a logistic regression model
model = LogisticRegression()

# Train the model
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate accuracy score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

This code imports the necessary modules, splits the data into training and testing sets using `train_test_split`, creates a logistic regression model using `LogisticRegression()`, trains the model using `fit`, makes predictions on the test set using `predict`, and calculates the accuracy score using `accuracy_score`.

Make sure to replace `X` and `y` with your actual data and labels. Additionally, you may need to preprocess your data before applying logistic regression.

I hope this helps you get started with logistic regression in Python! Let me know if you have any further questions.

from sklearn.model_selection import train_test_split, GridSearchCV X_train, X_test, y_train, y_test = train_test_split(aac_all,label_all,test_size=0.2) from sklearn.linear_model import LogisticRegression from sklearn import metrics #First, an example for logistics regression cs = [1,3,5,7,10] param_grid = dict(C = cs) cls = LogisticRegression() grid = GridSearchCV(estimator=cls, param_grid=param_grid,cv = 5,scoring ='roc_auc') grid.fit(X_train, y_train) print("grid.best_params_") print(grid.best_params_) print("Best auc_roc on train set:{:.2f}".format(grid.best_score_)) print("Test set auc_roc:{:.2f}".format(grid.score(X_test,y_test))) y_predict = grid.predict(X_test) TN,FP,FN,TP = metrics.confusion_matrix(y_test, y_predict).ravel() recall = TP/(TP+FP) y_prob = grid.predict_proba(X_test) auroc = metrics.roc_auc_score(y_test, y_prob)

这段代码是一个使用逻辑回归进行分类任务的示例。首先，它导入了需要的库和函数：`train_test_split`用于将数据集分割为训练集和测试集，`GridSearchCV`用于进行网格搜索交叉验证，`LogisticRegression`用于创建逻辑回归模型，`metrics`包含了一些评估指标。

接下来，代码使用`train_test_split`将数据集`aac_all`和`label_all`分割成训练集和测试集，其中测试集占总数据集的20%。

然后，代码定义了一个逻辑回归模型，并创建了一个参数网格`param_grid`，其中包含不同的正则化参数C的值。接着，使用`GridSearchCV`进行交叉验证和网格搜索，选择最佳的模型参数。最后，打印出最佳参数、在训练集上的最佳AUC-ROC评分以及在测试集上的AUC-ROC评分。

接下来，代码使用最佳模型在测试集上进行预测，并计算混淆矩阵和召回率。最后，使用预测的概率值计算AUC-ROC评分并打印出来。

请注意，代码中的`print(grid.best_params_)`和其他打印语句是为了展示结果，在实际使用时可以根据需要进行修改或删除。