Model Accuracy in Python
In this template, we classify points using the logistic regression method provided by statsmodels. Afterwards, we create a confusion matrix that makes it easier to see the correctly and incorrectly classified classes. Some other related topics you might be interested in are Logistic Regression with statsmodels in Python, Confusion Matrix with statsmodels in Python, Logistic Regression Curve in Python. The Model Accuracy in Python template is among the topics covered in detail in the 365 Data Science program.
Who is it for
This is an open-access Python template that is going useful to Data Analysts, Data Scientists, Machine Learning Engineers, and anyone who is interested in learning how to solve classification problems.
How it can help you
Accuracy is one of the metrics one can use to evaluate the performance of a classification model. It is defined as the number of correctly predicted items over the number of total items in the data set. Very often, accuracy is represented as a percentage. This template can be exactly used to calculate the accuracy of a classification model.