Challenges

Name: Challenges
Author: Briana Brownell

Learn how to choose the right model and how to deal with common challenges in classification modeling

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Course: On-Demand

So let's compare the classification models that we've seen so far, and figure out how we might go about choosing the right model for a specific classification task. So I've got all of the modules imported here from sklearn. They're all labeled, and all of the modules are being input here. So let's run that, and then we can see how they all compare. So overall, you can see that some of the styles of classifier have worked on our synthetic data sets much better than some of the other ones. Just generally, the linearly separable synthetic data set has higher accuracy across the board. And that makes sense, because depending on the style of model, we may not have a way to understand more complex relationships in the data. So it's all done quite well for this linearly separable example. But where it gets interesting is the difference between the sort of concentric circles and the two moons. So the ones that did the best on interpreting the very strange shape of the two moons data set were also the ones that were much more difficult to explain. So for example, the neural network did well, and the support vector machine did well when we used the more complicated kernel. Remember also that one of the challenges with the support vector machines was that it took a long time for it to train. Similarly, neural networks also take a lot longer in order to train, and they require a lot more data to be trained effectively. So you can see that there's often a trade-off between explainability and accuracy. The more accurate that you get in a highly complex classification task, the harder it tends to be to explain. Meanwhile, you can have a set of relatively simple rules in the case of a really complex data structure. You only get part of the way to the finish line in terms of accuracy. So when you're deciding which algorithm to use in your classification modeling task, you should think about what kinds of qualities are important to you. So is, for example, explainability more important than overall accuracy? Does the amount of training time and the amount of data needed change the optimal decision for your specific case? There really is no right answer for which model is the best in all cases. It all completely depends on the use case and the factors that are important in your modeling task. But there is one concept that is going to be important for all of your modeling tasks. And that's making sure that you have a balance between underfitting the data and overfitting the data. So you want to try to make sure that you are recognizing the true features of the data but not being so brittlely dependent on that training set that you get spurious predictors that when you generalize to more data that's coming in, all of a sudden that element doesn't do a very good job of predicting your class anymore. How you find this fit is a challenging task that takes experience. Overall, you want to make sure you balance the complexity of the model with the features that are truly in the data that you want to be able to extract. Now you should have an overview of some of the trade-offs that you might need to make in terms of choosing which model is best for your classification task.

Intermediate Provider : Briana Brownell 6 Lessons · 16m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, you will learn how to choose the right model by considering the trade-offs between them. You will also learn how to deal with common challenges faced in classification models, including bias, fairness, and data collection problems.

These lessons will explore the balance between accuracy and explainability, as well as remaining aware of groups your models can affect. You will learn about some of the current problems with classification systems in terms of adversarial attacks that can destroy model performance and cause major problems in deployment. Finally, you will learn about emerging challenges and potential legislation in classification modeling.

What You'll Learn

Choose the right classification model by weighing the trade-offs between different models
Address common challenges in classification models, including bias, fairness, and data collection problems
Balance accuracy against explainability when building classification models
Identify the groups your models can affect and remain aware of their impact
Recognize adversarial attacks that can destroy model performance and cause problems in deployment
Explore emerging challenges and potential legislation in classification modeling

Key Takeaways

Choosing a classification model involves weighing the trade-offs between the available models.
Classification models face common challenges such as bias, fairness, and data collection problems.
There is a balance to consider between a model's accuracy and its explainability.
Adversarial attacks can destroy model performance and cause major problems in deployment.
Classification modeling faces emerging challenges and potential legislation.

Frequently Asked Questions

What does this course cover?

The course covers how to choose the right classification model by considering trade-offs, common challenges such as bias, fairness, and data collection problems, the balance between accuracy and explainability, adversarial attacks, and emerging challenges and potential legislation in classification modeling.

What topics are included in the lessons?

The lessons include Choosing the Right Model, Data collection problems, Explainability, Bias and Fairness, Adversarial Attacks, and Emerging Challenges.

What skills will I gain from this course?

The course develops skills in Data Classification, Feature Learning, Learning Metrics, Machine Learning, Machine Learning Model Training, and Statistical Classification.

Does the course address fairness and bias in models?

Yes. The lessons cover bias and fairness as common challenges in classification models and emphasize remaining aware of the groups your models can affect.

Does the course discuss security risks to classification models?

Yes. It covers adversarial attacks that can destroy model performance and cause major problems in deployment.