Evaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores - unable to understand which model is performing well.
Wondering where evaluation metrics fit in? Here’s how the typical machine learning model building process works:
- We build a machine learning model (both regression and classification included)
- Get feedback from the evaluation metric(s)
- Make improvements to the model
- Use the evaluation metric to gauge the model’s performance, and
- Continue until you achieve a desirable accuracy
Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.
If you’ve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well - you’ve come to the right course!
- What is an evaluation metric?
- What are the different types of evaluation metrics?
- Do I really need to master evaluation metrics to understand machine learning?
- What kind of evaluation metrics questions can I expect in an interview?
- How do evaluation metrics help me improve my hackathon rankings?
- Can I use the same evaluation metrics for regression as well as classification problems?
- Is cross validation an evaluation metric?
We have seen plenty of analysts and aspiring data scientists not even bothering to check how robust their machine learning 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 machine learning model is not the motive! It’s about creating and selecting a model which gives high accuracy on an out of sample data (or unseen data). Hence, it is crucial to check the accuracy of your model prior to computing predicted values.
This is where evaluation metrics help us.
- Types of Machine Learning
- Why do we need Evaluation Metrics?
- AI&ML Blackbelt Plus Program (Sponsored)
- Confusion Matrix
- Quiz: Confusion Matrix
- Quiz: Accuracy
- Alternatives of Accuracy
- Quiz: Alternatives of Accuracy
- Precision and Recall
- Quiz: Precision and Recall
- Quiz: AUC-ROC
- Log Loss
- Quiz: Log Loss
- Gini Coefficient
- MAE and MSE
- RMSE and RMSLE
- Quiz: RMSE and RMSLE
- R2 and Adjusted R2
- R2 and Adjusted R2
- The Way Forward
What is an evaluation metric?
The answer lies in the name itself! Evaluation metrics help us evaluate, or gauge, the performance (or accuracy) of our machine learning model. They are an integral part of the machine learning model building pipeline as we can iterate and improve our model’s performance by judging how it’s working.
What are the different types of evaluation metrics?
There are several types of evaluation metrics for machine learning models. The choice of the evaluation metric completely depends on the type of model and the implementation plan of the model.
The evaluation metrics varies according to the problem types - whether you’re building a regression model (continuous target variable) or a classification model (discrete target variable).
In this course, we’re covering evaluation metrics for both machine learning models. Here’s a taste of the different evaluation metrics you’ll find in the course:
- Confusion Matrix (Classification evaluation metric)
- F1 Score (Classification evaluation metric)
- AUC-ROC (Classification evaluation metric)
- Gini Coefficient (Classification evaluation metric)
- Root Mean Squared Error (RMSE - Regression evaluation metric), and many more!
Do I really need to master evaluation metrics to understand machine learning?
The short answer - yes. Evaluation metrics are critical to judging and improving our machine learning model’s performance. And who doesn’t want to do that?
Evaluation metrics are a must-know concept for every machine learning and data science aspirant.
What kind of evaluation metrics questions can I expect in an interview?
Here are a few common questions we’ve seen asked of beginners about evaluation metrics:
- What are the different types of evaluation metrics for regression and classification problems?
- Given a particular classification problem, should you use AUC-ROC or F1 Score (or something else)?
- What is precision and recall? How does that help in evaluating your machine learning model?
- What should you do if an evaluation metric is not working according to what you expected?
You’ll get a better idea about how to answer these questions inside the course.
How do evaluation metrics help me improve my hackathon rankings?
Evaluation metrics, as you might have guessed by now, will be of supreme importance in machine learning hackathons. Your ranking on the hackathon leaderboard will be based on the evaluation metric being used in that hackathon.
There’s no getting away from it - evaluation metrics are the lifeblood of your machine learning model’s performance.
Can I use the same evaluation metrics for regression as well as classification problems?
Not quite. Regression and classification models have their separate evaluation metrics. Remember , the evaluation metric depends on the target variable. If your target variable is continuous (aka a regression problem), you can’t use a classification metric to evaluate it!
Is cross validation an evaluation metric?
Cross validation is not technically an evaluation metric but we’ve still included this in the course. Cross Validation is one of the most important concepts in any type of machine learning model and a data scientist should be well versed in how it works.
Who should take the Evaluation Metrics for Machine Learning Models course?
This course is designed for anyone who wants to learn how to evaluate their machine learning models. So if you’re a newcomer to machine learning and want to improve your model’s performance, this course is for you!
I have decent programming experience but no background in machine learning. Is this course right for me?
Absolutely! We have designed the course in a way that will cater to newcomers and beginners in machine learning. Having basic knowledge about machine learning algorithms will be hugely beneficial for your learning.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Evaluation Metrics for Machine Learning Models” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Evaluation Metrics for Machine Learning Models” course in a few hours.
I’ve completed this course and have decent knowledge about evaluating machine learning models. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course to understand the end-to-end machine learning pipeline, and how evaluation metrics play a part there.
- Can I download the videos in this course?