The metrics that you choose to evaluate your machine learning algorithms are very important. Choice of metrics influences how the performance of machine learning algorithms is measured and compared. They influence how you weight the importance of different characteristics in the results and your ultimate choice of which algorithm to choose. In this post you will discover how to select and use different machine learning performance metrics in Python with scikit-learn. Metrics To Evaluate Machine Learning Algorithms in Python Photo by Ferrous Büller, some rights reserved.
A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to evaluate your models, you are likely to choose a poor model, or in the worst case, be misled about the expected performance of your model. Choosing an appropriate metric is challenging generally in applied machine learning, but is particularly difficult for imbalanced classification problems. Firstly, because most of the standard metrics that are widely used assume a balanced class distribution, and because typically not all classes, and therefore, not all prediction errors, are equal for imbalanced classification. In this tutorial, you will discover metrics that you can use for imbalanced classification. Tour of Evaluation Metrics for Imbalanced Classification Photo by Travis Wise, some rights reserved.
Keras metrics are functions that are used to evaluate the performance of your deep learning model. Choosing a good metric for your problem is usually a difficult task. Lucky for you, this article explains all that! In Keras, metrics are passed during the compile stage as shown below. You can pass several metrics by comma separating them.
A common mistake made by beginners is to apply machine learning algorithms to a problem without establishing a performance baseline. A performance baseline provides a minimum score above which a model is considered to have skill on the dataset. It also provides a point of relative improvement for all models evaluated on the dataset. A baseline can be established using a naive classifier, such as predicting one class label for all examples in the test dataset. Another common mistake made by beginners is using classification accuracy as a performance metric on problems that have an imbalanced class distribution.
Choosing a proper metric is a crucial yet difficult part of the machine learning project. In this blog post, you will learn about a number of common and lesser-known metrics and performance charts as well as typical decisions when it comes to choosing one for your project. I really wanted to make this post complete(ish) and covered a lot. Also, I wanted each metric section to contain everything you need (repeating some things here and there) so that you can just jump to the metric you are interested in and read that section alone. I know it is a lot to go over but if you want to make your guide "ultimate" you really have to go for it .