python 逻辑回归

Python中的逻辑回归-局限性 (Logistic Regression in Python - Limitations)

As you have seen from the above example, applying logistic regression for machine learning is not a difficult task. However, it comes with its own limitations. The logistic regression will not be able to handle a large number of categorical features. In the example we have discussed so far, we reduced the number of features to a very large extent.

从上面的示例中可以看出，将逻辑回归应用于机器学习并不是一项艰巨的任务。 但是，它有其自身的局限性。 逻辑回归将无法处理大量分类特征。 在到目前为止讨论的示例中，我们在很大程度上减少了特征数量。

However, if these features were important in our prediction, we would have been forced to include them, but then the logistic regression would fail to give us a good accuracy. Logistic regression is also vulnerable to overfitting. It cannot be applied to a non-linear problem. It will perform poorly with independent variables which are not correlated to the target and are correlated to each other. Thus, you will have to carefully evaluate the suitability of logistic regression to the problem that you are trying to solve.

但是，如果这些功能在我们的预测中很重要，我们将不得不将它们包括在内，但是逻辑回归将无法为我们提供良好的准确性。 Logistic回归也容易过拟合。 它不能应用于非线性问题。 在与目标不相关且彼此相关的自变量下，其性能会很差。 因此，您将必须仔细评估逻辑回归对您要解决的问题的适用性。

There are many areas of machine learning where other techniques are specified devised. To name a few, we have algorithms such as k-nearest neighbours (kNN), Linear Regression, Support Vector Machines (SVM), Decision Trees, Naive Bayes, and so on. Before finalizing on a particular model, you will have to evaluate the applicability of these various techniques to the problem that we are trying to solve.

在机器学习的许多领域中，还设计了其他技术。 仅举几例，我们有算法，例如k最近邻(kNN)，线性回归，支持向量机(SVM)，决策树，朴素贝叶斯等等。 在最终确定特定模型之前，您必须评估这些各种技术对我们要解决的问题的适用性。

翻译自: https://www.tutorialspoint.com/logistic_regression_in_python/logistic_regression_in_python_limitations.htm

python 逻辑回归