In computer vision, object detection is the problem of locating one or more objects in an image. Besides the traditional object detection techniques, advanced deep learning models like R-CNN and YOLO can achieve impressive detection over different types of objects. These models accept an image as the input and return the coordinates of the bounding box around each detected object. This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated. In another tutorial, the mAP will be discussed. In binary classification each input sample is assigned to one of two classes. Generally these two classes are assigned labels like 1 and 0, or positive and negative.
We feed the test image to the trained model, compares the predicted output with test image's label to evaluate either it's correct or wrong prediction. At the end, we will have the count of correct matches and the incorrect matches. The key realization we need to make, is that in the real world not all incorrect and correct matches hold equal value. Also in the real world, a single metric won't tell the complete story, that's why previously mentioned four metrics are used to evaluate the model. We could organize our predicted values compared to the real values in a confusion matrix.
Predictive models have become a trusted advisor to many businesses and for a good reason. These models can "foresee the future", and there are many different methods available, meaning any industry can find one that fits their particular challenges. When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). While data preparation and training a machine learning model is a key step in the machine learning pipeline, it's equally important to measure the performance of this trained model. How well the model generalizes on the unseen data is what defines adaptive vs non-adaptive machine learning models.
We have all worked on different kinds of Machine learning models, and each model needs to be evaluated in different ways. From the initial data that is provided to the outcome and the way, we as the users want to use it. A classification model would require a different metric for model evaluation as compared to a regression model or a Neural Net, and it's important to know and understand which metric to use and when. Here in this series, we go through some of these metrics, starting from the basic and the most commonly used ones to the application-specific and complex metrics that we can use. We will be starting with the basic metrics from sklearn and progress towards the more complicated metrics after that.
The confusion matrix is capable of giving the researchers detailed information about how a machine learning classifier has performed with respect to the target classes in the dataset. A confusion matrix will demonstrate display examples that have been properly classified against misclassified examples. Let's take a deeper look at how a confusion matrix is structured and how it can be interpreted. What Is A Confusion Matrix? Let's start by giving a simple definition of a confusion matrix.