this logisticregression
时间: 2023-09-25 07:08:28 浏览: 55
算法的原理是什么?
Logistic回归算法是一种分类算法,它利用逻辑函数对数据进行分类。该算法的原理是在给定输入特征的情况下,将一个事件分类为两个可能的结果之一,即二分类问题。它通过线性回归预测目标函数的值来实现分类,将线性预测结果映射到[0,1]区间中,然后利用阈值来判断预测结果的类别。通常可以使用梯度下降等方法来优化模型的参数,以提高模型的分类准确率。
相关问题
from sklearn.linear_model import LogisticRegression
Logistic Regression is a statistical method used for binary classification problems (classifying data into two categories). It uses a logistic function to model the probability of a certain input belonging to a certain category. In Python, you can use the scikit-learn library to implement Logistic Regression using the `LogisticRegression` class from the `linear_model` module. Here's an example of how to use it:
```
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
# Load iris dataset
iris = load_iris()
# Create logistic regression object
lr = LogisticRegression()
# Fit the model to the data
lr.fit(iris.data, iris.target)
# Predict new data
new_data = [[5.0, 3.6, 1.3, 0.25], [6.5, 3.0, 5.2, 2.0]]
predictions = lr.predict(new_data)
print(predictions)
```
In this example, we load the iris dataset and create a `LogisticRegression` object. We then fit the model to the data and predict the class of two new data points. The output should be an array with the predicted classes (0, 2) for the two new data points.
'LogisticRegression' object has no attribute 'pvalues'
This error message indicates that you are trying to access the 'pvalues' attribute of a 'LogisticRegression' object, but this attribute does not exist.
In logistic regression, the 'pvalues' attribute is not directly available. Instead, you can compute the p-values for the coefficients of the logistic regression model using a statistical test such as the Wald test or the likelihood ratio test.
To do this, you can use the 'summary()' method of the 'LogisticRegression' object to obtain a summary table of the model coefficients, which includes information such as the coefficient estimates, standard errors, z-scores, and p-values. For example:
```
from sklearn.linear_model import LogisticRegression
# Fit logistic regression model
model = LogisticRegression().fit(X, y)
# Compute summary table of model coefficients
summary = model.summary()
# Print summary table
print(summary)
```
This will output a summary table of the model coefficients, which includes the p-values for each coefficient. Note that the output format may vary depending on the version of scikit-learn that you are using.
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