python 逻辑回归_Python中的逻辑回归-摘要
python 逻辑回归
Python中的逻辑回归-摘要 (Logistic Regression in Python - Summary)
Logistic Regression is a statistical technique of binary classification. In this tutorial, you learned how to train the machine to use logistic regression. Creating machine learning models, the most important requirement is the availability of the data. Without adequate and relevant data, you cannot simply make the machine to learn.
Logistic回归是二进制分类的一种统计技术。 在本教程中，您学习了如何训练机器以使用逻辑回归。 创建机器学习模型时，最重要的要求是数据的可用性。 没有足够的相关数据，您将无法简单地学习机器。
Once you have data, your next major task is cleansing the data, eliminating the unwanted rows, fields, and select the appropriate fields for your model development. After this is done, you need to map the data into a format required by the classifier for its training. Thus, the data preparation is a major task in any machine learning application. Once you are ready with the data, you can select a particular type of classifier.
拥有数据后，下一个主要任务是清除数据，消除不需要的行，字段，并为模型开发选择适当的字段。 完成此操作后，您需要将数据映射为分类器训练所需的格式。 因此，数据准备是任何机器学习应用程序中的主要任务。 一旦准备好数据，就可以选择特定类型的分类器。
In this tutorial, you learned how to use a logistic regression classifier provided in the sklearn library. To train the classifier, we use about 70% of the data for training the model. We use the rest of the data for testing. We test the accuracy of the model. If this is not within acceptable limits, we go back to selecting the new set of features.
在本教程中，您学习了如何使用sklearn库中提供的逻辑回归分类器。 为了训练分类器，我们使用大约70％的数据来训练模型。 我们将其余数据用于测试。 我们测试模型的准确性。 如果这不在可接受的范围内，我们将返回选择新的功能集。
Once again, follow the entire process of preparing data, train the model, and test it, until you are satisfied with its accuracy. Before taking up any machine learning project, you must learn and have exposure to a wide variety of techniques which have been developed so far and which have been applied successfully in the industry.
再一次，遵循准备数据，训练模型和测试的整个过程，直到您对它的准确性感到满意为止。 在进行任何机器学习项目之前，您必须学习并接触到目前为止已经开发并且已经在行业中成功应用的多种技术。
python 逻辑回归