Machine Learning Tutorial Python - 10 Support Vector Machine (SVM)
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- เผยแพร่เมื่อ 27 มิ.ย. 2024
- Support vector machine (SVM) is a popular classification algorithm. This tutorial covers some theory first and then goes over python coding to solve iris flower classification problem using svm and sklearn library. We also cover different parameters such as gamma, regularization and how to fine tune svm classifier using these parameters. Basically the way support vector machine works is it draws a hyper plane in n dimension space such that it maximizes the margin between classification groups.
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Code: github.com/codebasics/py/blob...
Exercise: Open above notebook from github and go to the end.
Exercise solution: github.com/codebasics/py/blob...
Topics that are covered in this Video:
0:00 Introduction
0:20 Theory (Explain support vector machine using sklearn iris dataset flower classification problem)
3:11 What is Gamma?
4:21 What is Regularization?
5:27 Kernel
6:32 Coding (Start)
18:08 sklearn.svm SVC
21:41 Exercise (Classify hand written digits dataset from sklearn using SVM)
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Exercise solution: github.com/codebasics/py/blob/master/ML/10_svm/Exercise/10_svm_exercise_digits.ipynb
Complete machine learning tutorial playlist: th-cam.com/video/gmvvaobm7eQ/w-d-xo.html
I used model = SVC(C=2.0, gamma='auto', kernel='rbf') and got an accuracy of 100%
Can you check that it is right or not?
Also I used random_state = 100 in train_test_split method for random values
Thanks so much for the detailed video on SVM. This helped me a lot!
Thank you very much for these videos. They are really helpful. I did the exercise and got 99% when C=4. Any increase in C did not affect the accuracy. Also, any alteration made to gamma and kernel dropped the accuracy drastically. Thank you once again.
model = SVC(kernel = 'rbf', C = 4, gamma = 'scale')
With the above config, I got a model score of about 99.17%. Test size was 20%, as mentioned.
Thank you, these tutorials are amazing! :) cheers!
again great job sagnik. I am seeing that you are on the roll and finishing all the exercises from this playlist. keep it up :)
@@codebasics high model score leads to overfitting? as I got 98% model score with 60% training size
I got 99.25% model score with 70% training size
A very solid, informative yet concise tutorial. Excellent. Please keep it up.
What a wonderfull tutorial!! well done and well explained. Thanks a lot dude for the sharing of this expensive knowledge.
Got 1.0 score when C=4 for iris data set. Thank you Sir! Your machine learning Playlist is a boon for beginners like me.
That's not always a good thing though. In most real life problems, that would mean that your model has become overfitted.
Same here😱
@@nikitakazankov4099 Ikrt😏
@@nikitakazankov4099 Though it does make sense, Whenever I see a Russian name I bow down because of their intelligence.
@@nikitakazankov4099 bro, The accuracy is on test dataset. if it's on training dataset then it must be overfit
one of the best lecture I have ever watched
Hey Abhishek.
Great thanks for your kind words.Stay in touch for more videos and share our channel if you really find it worth.
Hello sir, thank you for your videos. It really helps from the beginner of the video which you have listed in data science playlist. 😄
The model in default method is 99.65% in train and 99.4% in test. Whereas gamma method will lower down the accuracy of the model from 99.4% to 75% therefore it has explicit shows the gamma method is unsuitable for the scenario however the regularisation has improve the train set to 1 and testing set is retained the best accuracy of model.
Yet, kernel parameter as linear has also provided a good accuracy of model.
Thank you for your guidance.
This series is the best I have seen on simple and explicit Machine learning and Algorithm.Thanks
Glad to hear that!
Thank you so much for your presentation. I have learned a lot.
Exercise
Test size=0.2, C=1, kernel='poly
Accuracy: 99.17%
Very very good tutorial. The gentle practice of svm. Thank you
Thank you for this. They are really helpful. I did the exercise and got 99.17% when C=10. Any increase in C did not affect the accuracy. Also, any alteration made to gamma and kernel dropped the accuracy drastically. Be blessed.
I was looking for python code to SVM... Thanks a lot... this was a great help... very clean and intuitive lecture~!
Glad it was helpful!
Very well-explained video. Thank you!
This is great! Thank you so much for the video
Wow! how brilliantly working and good teaching method as well . thx sir from Pakistan ... keep it Up!
**** pk
Your all concepts are so brilliant and well defined.because of these video , my concepts and doughts are now so much clear.
Glad you like them!
Thank you for this great series!
Thankyou so much for the wonderful job!!
hello great videos, loved this series. Can you please do a video on imbalanced data sets in classifications problems? Maybe just add onto a previous example you have but with a case where there are very few "1" or "true" values compared to "0" or "false" . thanks for you consideration!
your lectures are so addictive I am enjoy learning, thank you soooooooooo much
Your teaching skills are unmeasurable and it's very easy to understand no need to scratch our head for looking at some other training institute.
I have executed load_digits datasets and found the following score:
For 'rbf' kernal, score -98
'linear' kernal, score -97
Siddu, thanks for complement and good job on exercise. 👏👏👏 That is indeed a nice score
After all possible regularisations, my highest accuracy is 99%. Thank you sir
Iradukunda, that's a pretty good score buddy. Good job 👍👌👏
Great videos Bro, Finally understands something :)
Thank you so much for this clear and helpful explanation. well done
Thanks!!!!!!!!! for this wonderful tutorial got accuracy 99.166%
Great! Sir, Can you elaborate something about plotting the hyperplane (the decision function) in matplotlib
I want to see the best line which classifies the data
In linear kernel score is 96.9 percent and in rbf kernel score is 40 percent...
With gamma value the score is 0.06... And with the regularization value the score is around 45. 83 percent
Excellent video, I'm doing a review of what i learned a year ago in a deep learning course in the university (i'm a geophysics graduate) with this playlist without seeing too much math.
For C = 25 kernel = rbf and gamma = scale, Test_size = 0.2
Accuracy = 99.70%
I used kernel = linear and it gave me an accuracy score of 1.0 :3
Can you make a video on title "how to determine which classification model to be used in ML according to dataset" ?
Pycaret answers that query
@@nivedhansenthilkumar964 Thanks
Look up cross validation.
@@nivedhansenthilkumar964 thanks
Yess
When I did the exercise, rbf performs slightly better for me than linear. I believe when you created your notebook, the default gamma was auto. Using the scale option provides much better results than auto for rbf.
very much helpful for beigineers !! Thank you so much..
It was really really helpful, thanks a million.
I got 98.16% accuracy with C=2, kernel=rbf and gamma=0.001
Maximum Accuracy: 100%
Minimum Accuracy: 95 %
Avg Accuracy: 98.16%
That’s the way to go Anurag, good job working on that exercise
I got score 99.4 when c=1 and gamma=scale
And i got 50 when gamma = auto
And 99.7 when gamma = auto and c=10
Thank you sir for this series. And following the tutorial with doing exercises
That’s the way to go irshad, good job working on that exercise
@codebasics, sir could you please make a video with regression models like KNN regression or random forest with train_test_validation set? Thank you for your amazing videos..I started my machine learning implementation journey with your tutorials.❤
You teach so well...i thought i will never understand ml...
What an awesome tutorial.
Thank you so much Sir! for your machine learning playlist
I am happy this was helpful to you.
Great vid! but would've been nice if you had plotted the SVM line and scatter plots. Also running a few predictions would be useful.
For digits I got highest accuracy value as 0.99 with gamma 'scale' and C=10
Thank you for your video!
That’s the way to go Коробка, good job working on that exercise
You are seeming to be tired from your voice but hats off your efforts !
thanks for this!!
Tried couple of iterations finally I got 99.166% accuracy with all default parameters. random_state=1 while defining train test data.. Thanks a lot Sir
Nice work!
And thank you sir for an awesome playlist
Can't thank you enough bro.💜🙏
Jai Shree Ram. Hope Ram bhagwaan bless your entire family.
I calculated on digits dataset and comes with SVC = 99.16%
while with logistic regression it was = 96.38%.
So kudos to Support Vector Classification.
my score is 76.5 with ginni index model and 75.9 with entropy model
btw thanks for good teaching sir ji
All your videos are just awesome❤❤❤
Thanks for your kind words of appreciation
I am liking the tutorials Thanks
Glad you like them!
great tutorial man👍👍👍
Please make a video on the topic "How to choose which ML algorithm for a dataset".
And thanks for amazing videos, sir.
Thank You Sir, Dhaval and for the exercise I used normal rbf kernel C= 1 and got the accuracy of 0.991668
Hello sir, thank you so much for this video. I got 99.25% when i put C=1.
If, I use kernel='rbf' then, I got 99 % accuracy.
And for kernel='linear', I got 97.7% accuracy.
heartfull thanks to you sir
Excellent...!!!! 😀 thanks
Roopa, thanks for the feedback
When i tried iris data set with SVC default values, i got 1 accuracy. Digits data set with SVM(kernal='linear') gave 98% accuracy.
dank je wel
Logistic Regression is giving the 100% score.....its performing better than SVC and also Decision Tree.
Model score when C=20 is 0.9944444444444445. Varying kernel, gamma gave lower scores. This was my best.
used hyperparameter tuning here to get 100% for train and 99.72% for test...luckily data was clean cause im not very experienced in data cleaning and here i didnt even do too much data visualization
thank you so much:)
SVC(C=4, kernel='rbf',gamma='scale')
Got an accuracy of 99.17%
I got accuracy of 99.72% by keeping kernel='rbf', C=1 and gamma=0.002
ur the best broo
BEST DEMO ON SVM
thanks a lot for uploading. Plz try to upload next vides soon.
Exercise Results:
C=5, kernel ='rbf' & test size used 0.2, Accuracy: 99.5%
great thank you
amazing video
We have done with the iris data in Logistic Regression Exercise , which peak value was also 96 %
I got a peak value of 1 in LR
How to find linear or non-linear in the dataset if we get very large dataset ?
Thank you
Hi, can we change columns using one hot encoding or just only encoder?
Sir please make videos on unsupervised learning waiting for it for a long time hope you will help us.
Very good tutorial. I got 99.9% accuracy using kernel='rbf' and C=1.0.
That’s the way to go Ajeniyi, good job working on that exercise
Can you do some quick videos on exploratory data analyses? Things like custom querying and displaying relation between queried data elements?
Really nice video. Thank you so much such brilliant video. I got the score of 98.61% with C = 1. but i could not apply matplotlib visualization as there are 64 columns. I could not understand which columns should be selected for visualization.
Thanks for your effort sir, but there is something I wonder.. When I fit a model, I can't see any description like you have in your jupyter notebook.(C=1, cache_size=200 etc..) I can't see them.. is there any way to see them?
model=SVC(C= 5, gamma= 'scale', kernel= 'rbf')
train size was 80%
score=99.45%
Just watching the tutorial, you are not going to learn anything :-) --> We understood your intention sir. A big salute to you.
Ha ha.. nice. It is very true.
Thank you sir for wonderful explanation.I think high regularization means simpler the model.(5.11)
got a score of 99.16 for my test samples with C=any thing more than 2( i wonder why there were no difference between C=2 and C= 100, i got the 99.16 accuracy for all the values for C more than 2!). didn't change the gamma or the score would be destroyed! the kernel ='rbf'.
thanks for this amazing tutorial BTW! :)
Good job Amir, that’s a pretty good score. Thanks for working on exercise
very nice tutorial!
😊👍
Do you know a way you could look at only one data points specifically when you do the prediction at the end?
Thanks
got 100% accuracy for Digit dataset where model=SVC(C=7,kernel='rbf')
1)What is good to have- a large gamma or regularization parameter?
2)We used only fit() but not fit_transform(), is it because the rbf Kernel will perform the transformation itself to scale the features and the target labels?
I think it should be high C corresponds to low regularization, which means the classifier don't penalize too much on classification error.
Vice versa for low C.
Usually we predict the values based on x_test value and we get some predictions for x_test values. And after that we compare the y_pred with the y_test, but in the video the predict code is not written or is it like that only ??
rbf kernel gives around 98.9% accuracy while, linear kernel gives 98.3% accuracy in my calculation
excellent video
Glad you liked it!
Thanks for the video! It's very useful and clear. Can we get the same for other algorithms like Navie bayes, KNN, ANN etc...?
Priya I am happy you liked it. I already have tutorial on naive Bayes. Check out machine learning playlist on my channel. I am also continuing deep learning series so the other topics you suggested will be covered as well.
SVC(kernel='rbf' , C=10)
accuracy = 0.9933333333333333
(digit classifier)
thank you so much sir
i am your fan from india
guru dev ko sadar naman
Sir, how to add legend displaying all the three categories with corresponding markers in the plot?
What if we do sliding window to get dimensionality reduction (reduce the length of data), then how to classify the data if the length of data and target is different?
I got a 98.61 % accuracy. Gamma was set to "scale" and C was 1.
Same For me
The best