Evaluation metrics have a correlation with machine learning tasks. The tasks of classification, regression, ranking, clustering, topic modelling, etc, all have different metrics. Some metrics, such as precision, recall, are of use for multiple tasks. Classification, regression, and ranking are examples of supervised learning, which comprises a majority of machine learning applications. In this blog, we'll be focusing on the metrics for supervised learning modules.
This article was originally published in February 2016 and updated in August 2019. The idea of building machine learning models works on a constructive feedback principle. You build a model, get feedback from metrics, make improvements and continue until you achieve a desirable accuracy. Evaluation metrics explain the performance of a model. An important aspect of evaluation metrics is their capability to discriminate among model results. I have seen plenty of analysts and aspiring data scientists not even bothering to check how robust their model is. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach. Simply building a predictive model is not your motive. It's about creating and selecting a model which gives high accuracy on out of sample data.
Clustering (cluster analysis) is grouping objects based on similarities. Clustering can be used in many areas, including machine learning, computer graphics, pattern recognition, image analysis, information retrieval, bioinformatics, and data compression. Clusters are a tricky concept, which is why there are so many different clustering algorithms. Different cluster models are employed, and for each of these cluster models, different algorithms can be given. Clusters found by one clustering algorithm will definitely be different from clusters found by a different algorithm. Grouping an unlabelled example is called clustering. As the samples are unlabelled, clustering relies on unsupervised machine learning. If the examples are labeled, then it becomes classification. Knowledge of cluster models is fundamental if you want to understand the differences between various cluster algorithms, and in this article, we're going to explore this topic in depth.
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.
Note: Before starting Part 3, be sure to read Part 1 and Part 2! In this final installment of Visual Diagnostics for More Informed Machine Learning, we'll close the loop on visualization tools for navigating the different phases of the machine learning workflow. Recall that we are framing the workflow in terms of the'model selection triple' -- this includes analyzing and selecting features, experimenting with different model forms, and evaluating and tuning fitted models. So far, we've covered methods for visual feature analysis in Part 1 and methods for model family and form exploration in Part 2. This post will cover evaluation and tuning, so we'll begin with two questions: You've probably heard other machine learning practitioners talking about their F1 scores or their R-Squared value. Generally speaking, we do tend to rely on numeric scores to tell us when our models are performing well or poorly. There are a number of measures we can use to evaluate our fitted models.