Machine Learning: Testing and Error Metrics
Vložit
- čas přidán 25. 07. 2024
- Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
A friendly journey into the process of evaluating and improving machine learning models.
- Training, Testing
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
- Types of Errors: Overfitting and Underfitting
- Cross Validation and K-fold Cross Validation
- Model Evaluation Graphs
- Grid Search
For a code implementation, check out this repo:
github.com/luisguiserrano/man...
0:00 Introduction
0:37 Which model is better
1:31 Why Testing?
3:27 Golden Rule # 1
4:21 How do we not 'lose' the training data?
4:38 K-Fold Cross Validation
5:20 Randomizing in Cross Validation
5:38 Evaluation Metrics
7:53 Medical Model
8:05 Spam Classifier Model
9:25 Confusion Matrix Diagnosis
11:50 Accuracy
19:47 Precision and Recall
20:54 Credit Card Fraud
22:36 Harmonic mean
24:08 F1 Score
27:16 Types of Errors
27:56 Classification
30:03 Error due to variance (overfitting)
30:18 Error due to bias (underfitting)
31:45 Tradeoff
37:55 Solution: Cross Validation Testing
39:16 Training a Logistic Regression Model
40:04 Training a Decision Tree
40:49 Training a Support Vector Machine
41:14 Grid Search Cross Validation
41:59 Parameters and Hyperparameters
42:56 How to solve a problem
43:20 How to use machine learning
44:04 Thank you! - Věda a technologie
Your online tutorials are really great. You demonstrate intimate understanding of the subject and deliver it so so well. It's just left for me to take it in. Congratulations on this series. Immense help to students.
Thanks Luis. This is the simplest and best ever tutorial in ML I have come across.
Incredible video! I always find remembering evaluation metrics difficult but this is a really great way to get my head around it in a memorable way! Will definitely be watching all the videos you put out - thanks in advance.
Your Videos are one of the best on the web, please keep it coming.. Thanks
Awesome! What a way to simplify the complex concepts! You validated once again that you are awesome and the hyperparameter used is your pedagogic style!
Your videos are without any doubt the most easy to follow and easy to understand out there! Thanks for explaning things in a simple way so it finally makes sense!
Love the way you teach and make complex things so simple. Thanks a lot Luis. Hoping to see more videos such.
One of the best explanations on ML. Amazing clarity of thought. You have a super visual mind and thank you Luis for sharing what you see with us. Ever grateful 🙏
This just answered so many questions I had about ML accuracy scoring and HOW the background code in R-Studio (many built-in functions) are actually calculating accuracy percentages. Thank you for this video
Awesome Luis, it is the simplest and easiest tutorial for ML I have ever see from CZcams, thanks again. Keep it up.
Brilliant explanation. Thanks for taking the time to make the video.!!
Thanks Luis! This is the most concise introduction to those terminologies one could find on the web! Great job. You saved me tons of time! Thanks a lot!
Thank you, Weiran, glad you liked it!
@@SerranoAcademy Hey, could you please explain the difference between in-sample risk and training error? And how we can measure the in sample risk thus, we can compute the optimism of the dataset?
You are the best. You explain better than my Machine Learning Lecturer. The use of images, colors and explanation are 10/10.
Hey Luis, I saw your ML videos, and they are awsome. I understood the core and core of it. wow...
Thanks maaaan...
Thank you for pointing me here. Done watching the entire video and despite that English is not my primary language, I am hooked and fascinated how you make the explanation exciting!
The visuals and your story telling is absolutely superb as well! Your channel is a God send!
Thanks for the great video. I really enjoyed all the videos you posted on ML. Hope more to come.
Thank you Chaoli! Just added a CNN video, check it out!
This is so easy to understand. Just the best tutorial ive seen for this topic!
Awesome bite-sized videos Luis, really intuitive to understand! Great job!
concise, vivid. one of the greatest tutorials I have come across.
I would thank you first. The way that you are simplifying and giving examples is really great. Well done :)
I like the discussion of recall/precision. I was always confused by how to get an intuitive explanation of recall and precsion, now it's clear.
Very helpful. Great Explanation!!!!! Thanks a lot :)
Would love to have more videos giving clarity of statistics concepts used for machine learning .
Haven't watched the video, but I am pretty sure it would be amazing. You are doing amazing service to so many people. Thanks.
This is extremely good, nicely made and presented. Thanks a lot.
Luis ! Great Course ! best one out there . I will recommend the IBM QMS Quality Engineers to view this video! thanks !
Thank you Luis! Really appreciate your works.
I enjoyed all the videos you posted on ML. Great videos. They make me better understand a lot of concepts and terms that I head/read in my efforts to learn how AI works. What I would realy like to see and understand now, is how these concepts are translated into code (tenser flow or some other framework). Thank you for your videos.
Your videos are awesome. That's my 3rd watched video today, made by you. This is really awesome since it clears the basics in an interesting and simple way. I wish I had watched it earlier. That'd have helped me in my previous vivas. :P
One of the best explications, please keep it up. Subscribed :)
Awesome ML training! Thank you, Luis!
Great video! Going to check out the rest of them.
Video should get way more likes and views, very detailed but in a simplfied way. Thanks a lot for the great info!
Thanks a lot, you made very easy to understand metrics
Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!
Thanks Luis, A very well presented explanation.
Thanks Luis for this detailed explanation! Request you to upload videos on some other expansive metrics too such as AUC, ROC etc.
Thanks Luis for making, model evaluation attributes simpler
Great video, great explanation!!
Thanks Luis!!
Best explanations I have ever seen regarding ML. Thanks a lot for Your effort
Awesome examples & explanation. TY
Thanks for sharing this wonderful tutorial.
Thanks man !!!! This is so very helpful !!!!
Thanks Luis! This very well summarize the model selection in a very concise manner. Can you please do a video on various metrics to assess a machine learning models, like lift chart, ROC curve, confusion matrix etc. all combined and their use in different cases?
Very helpful video, thank you very much!
Hi Luis , Many thanks for these interactive videos & very nice explanation of the complex topics on data science ...You are very clear about the fundas of the subject ...I request you to kindly help us with Random Forest techniques & SVM . Thanks in advance
Wooh great and detailed explanation thank you, it is easy to follow, I would love to know your thoughts on when to use AUC metrics for model testing
Thanks Luis. Exceptional delivery. You really have an innate understanding of these concepts and algorithms. Thanks a lot
Luis great another masterpiece "Machine Learning: Testing and Error Metrics" thanks
Thank you so much! Another amazing tutorial!!!
Amazing video series!!
great series made easy to comprehend thank you
Awesome explanation. Congrats.
Great lecture Luis ! Thanks !!!
Thanks, great explanation!
Hello Luis, First of all your videos are great. Thanks a lot.
In the video, the precision for credit card fraud detection is 0/0 right? how come it is 100%? Am I missing something here?
Muchas Gracias, Luis!
Thank you very much for the easy and amazing explanation
I am very much thankful to you for so nice videos.
Thanks Lusis. Really helpful!
Thank you very much. Very informative. Going to try udacity courses now!
great job, it is so useful and simple thanks so much, but how can I find parameters and hyperparameters for other algorithms such as Naïve bays and K-nn
Thank you Luis! You and the material you use are really good for teaching. They increase my interest! I alteady bought your book "Grokking Machine Learning" in digital version. Can't wait to read it and work with it!
Thank you so much Georgina! I hope you enjoy it! :)
Thank you Luis. Well explained
Great video. Great explanation.
Gracias Luis ; excelente trabajo . Salu2 desde Argentina .
Awesome explanation !
Thank you so much Luis, grt session 👍🏻
Increíble Luís, no hay nada similar ni en udemy ni en youtube. Felicidades por conseguir se tan eficaz a la hora de transmitir tus conocimientos y mil gracias por brindarnos este contenido. Te deseo muchos éxitos!
Muchas gracias Juan! Que lindo mensaje, me alegra que te guste el contenido. Abrazo!
I have really appreciated this video. It helped me understanding I was facing overfitting in my project. Would you mind doing another video about regression problems? Thank you so much in advance.
really good! Thanks!
Great explanation 🙏
Thanks for the great tutorial
So awesome, thank you very much!
This is brilliant! Thank you!!
Wow..I don't think there is a better explanation than this
Wow! Brilliant session
Awesome intro on ML model evaluation
Nobody else explained things as clearly as you did.
Great explanation, Thanks
Great stuff Luis.
Nice summary video
Great explanation keep it up
Very good and helpful. Easy to understand in the beginning
, at the end a little bit too fast about too much new things
.
so clean explanation
Muy buen trabajo, mas videos en español por favor. GRacias :)
Well Explained.
Love the three golden rules. Rule #4: don't forget the first three
thanks Luis !
You are awesome Luis.
This is amazing!!! Thank you sir...I am trying to learn ML since a long time...after so many complicated tutorials I lost interest in ML and went in web development . Now I found this tutorial and could grasp all those complicated terms with ease. I went to your Udacity ML nano degree course link but its expensive :(
Hi Parigha! Thank you for your kind message. Check out this free deep course that I teach with other people Udacity: www.udacity.com/course/deep-learning-pytorch--ud188
Very easily explained
very nice , thanks a ton
Thank you!
Very useful video
Thanks a lot!
Luis you are the best man!!!!
Brilliant sir.
Thanks, it is better if you can help to teach us on codes as well for some real Tensorflow and Keras example for image recognition. I like your style. Very clear as I had some basics but yours clarify my basics to better level.
Really helpful
Hello Luis, Thanks for the presentation, can you provide us with the slides in pdf if possible ?