Data Science
Measure the performance of our model
Let assume that we’re working on a simple classification problem using deep learning. We gave the picture (blueberry) as an input to the model and get our prediction results (with probability) as follows.
- cherry: 0.35
- raspberry: 0.25
- blueberry: 0.2
- strawberry: 0.1
- apple: 0.06
- orange: 0.04
How will you evaluate your model? Do you know about the difference between the top-1 and top-5 accuracy?
- Using top-1 accuracy, you count this output as
true, because it predicted a cherry. - Using top-5 accuracy, you count this output as
false, because blueberry is among the top-5 guesses.
We test the model on 5 images and get the following results.
Given this example, our model predicted correctly 2 images and the true label turns up 3 times in the top 5 predicted labels.
What is Accuracy?
It is one of the metrics to describe the accuracy of an algorithm on a classification task. Accuracy is the number of samples that are paired divided by the number of samples.
Accuracy = No of correct predictions / Total no of correcct predicitons
For example: If accuracy comes out to 91%, it means that 91 correct predictions out of 100 total examples.
Top-1 accuracy is the conventional accuracy, model prediction (the one with the highest probability) must be exactly the expected answer.
It measures the proportion of examples for which the predictedlabel matches the single target label.
In our case, the top-1 accuracy = 2/5 = 0.4.
Top-5 accuracy means any of our model’s top 5 highest probability answers match with the expected answer.
It considers a classification correct if any of the five predictions matches the target label.
In our case, the top-5 accuracy = 3/5 = 0.6.
Today, we have seen the difference between Top-1 Accuracy and Top-5 Accuracy. Keep in mind that: with N >= K then Top-N Accuracy >= Top-K Accuracy. In other words, with a higher the Top-N Accuracy can either get higher or remain the same.
Easy, right?
As an experienced data scientist deeply immersed in the field, I've honed my expertise through practical application and continuous learning. My proficiency extends across various domains within data science, including machine learning, deep learning, model evaluation, and performance metrics.
Regarding the concepts presented in the article "Measure the performance of our model" by Anh T. Dang on Towards Data Science, the piece delves into fundamental aspects of evaluating a classification model, specifically in the context of deep learning applied to a simple classification problem.
The article discusses model evaluation using the top-1 and top-5 accuracy metrics. Here's a breakdown of the concepts covered:
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Classification Problem Using Deep Learning: The scenario involves a classification task with a deep learning model where the input is an image (blueberry) and the model predicts probabilities for different classes (cherry, raspberry, blueberry, strawberry, apple, orange).
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Model Evaluation:
- Top-1 Accuracy: This measures whether the model's top prediction matches the single target label. In the example provided, the top-1 accuracy is calculated as 2 correct predictions out of 5, resulting in a value of 0.4 (2/5).
- Top-5 Accuracy: It considers a prediction correct if any of the top 5 predictions matches the target label. In the example, the top-5 accuracy is calculated as 3 correct predictions out of 5, resulting in a value of 0.6 (3/5).
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Accuracy Metric: The article defines accuracy as the ratio of correct predictions to the total number of predictions. For instance, an accuracy of 91% implies 91 correct predictions out of 100 total examples.
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Comparison between Top-N Accuracies: The article highlights that when N >= K, Top-N Accuracy will be greater than or equal to Top-K Accuracy. Essentially, as the number of considered top predictions (N) increases, the accuracy can either remain the same or improve.
By understanding these concepts, one gains insight into effectively assessing model performance in classification tasks, particularly in deep learning scenarios. The distinction between top-1 and top-5 accuracy metrics provides a nuanced view of model evaluation, considering different levels of prediction correctness.
This article serves as a foundational guide for individuals navigating model evaluation and helps in comprehending the intricacies of accuracy metrics in assessing classification models' performance.
