Applied Data Science Coding in Python: How to get Classification Accuracy

Classification accuracy is a measure of how well a machine learning model is able to correctly predict the class of a given data point. In other words, it tells us what proportion of the predictions made by the model are correct. It is a commonly used metric to evaluate the performance of a classification model.

In Python, there are several libraries that can be used to calculate classification accuracy. The most popular library for machine learning is scikit-learn. It provides a built-in function called “accuracy_score” that can be used to calculate the accuracy of a model.

To use the function, you first need to import it from the library and then pass in two arguments: the predicted class labels and the true class labels. The predicted class labels are the outputs generated by the model and the true class labels are the actual class labels of the data points.

The function will then compare the predicted class labels with the true class labels and return the proportion of correct predictions.

It is important to note that, in practice, accuracy may not always be the best metric to evaluate the performance of a classification model. This is because it does not take into account the imbalance of classes in the data set, and it does not distinguish between false positives and false negatives. Other metric like precision, recall, F1-score and ROC-AUC are also important to consider when evaluating a model’s performance.

In summary, classification accuracy is a way to evaluate the performance of a classification model by measuring the proportion of correct predictions made by the model. Python’s scikit-learn library provides a built-in function called “accuracy_score” that can be used to calculate classification accuracy, but it is important to consider other evaluation metrics as well.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Classification Accuracy.