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Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code
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- Опубликовано: 15 авг 2024
- PCA or principal component analysis is a dimensionality reduction technique that can help us reduce dimensions of dataset that we use in machine learning for training. It helps with famous dimensionality curse problem. In this video we will understand what PCA is all about, write python code for handwritten digits dataset classification and then use PCA to train the same model using PCA.
Code: github.com/cod...
Exercise: github.com/cod...
⭐️ Timestamps ⭐️
00:00 Theory
09:12 Coding
23:04 Exercise
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super helpful for newbies not scaring them off with too many statistical terms and getting overwhelmed. thank u so much
Awesome videos - Simple explanations. A balanced approach to teaching with a right mixture of theory and practicals and not overwhelming the learners . i loved the approach - After seeing numerous ML training videos from across the spectrum , this is far most the best one i have seen . Thank you for taking time to create these videos .
This is a really easy to understand and thorough explanation of principal component analysis. Many others I watched were either too technical and math theory oriented or to basic in showing how to use the function but not what it does. This is a great balance of understanding and practicality.
You are the best! I am doing PG in DS but still, I watch your videos for better understanding. Kudos! Keep it up!
The last few minutes were BANG ON! This is what i wanted to hear. Thanks!
This is the best channel on RUclips. You are simply amazing. You just saved my career. Thanks a million
@Karthiktanu I am.a student of data science and analytics
@@Thanusree234 yes
Thank you, PCA concept is clearly explained .
Need to understand in actual real life scenarios, what we consider, the performance or process time
Masters in Data Science in the UK and still loves watching only your videos :-)
I would really appreciate for your hard work in making these videos and decoding the complex to easy..
it's like the 10th video i'm watching on PCA and the FIRST one I understand, thank you so much!
Hello Code basics. I usually enjoy your videos as I learn a lot from them. Can you make a video on association rules, apriori algorithms and any machine model that deals with the determination of interrelationships amongst variables? Thank you
Here is my two question for u
1- why u dont scaled the target column means (y)
2- for score as accuracy why u dont compare with actual and predicted u give for score is x_test and y_test why not y_pred and y_test
at 17:35, shouldn't we be doing pca.fit_transform()on our scaled dataset (X_scaled in our case) rather than on X?
This came to my mind also.
Perhaps the accuracy would have been higher if he scaled before pca.
Yes it should be on X_scaled.
Thank you very much for simple and great explanation. I got higher accuracy in SVM=86.74 %, after PCA I got accuracy in RF=73.06
But timing and machine power also decreases heavily
can you share the github link for your code please.
Thanks sir the great work, your explanation makes ML easier for sure 🙏
It was an amazing explanation of PCA without much mathematics and eigen value and vector which scares me. Interesting learning 1. we can know variance explained by each PC which helps.
Ahhhhhh I've been struggling to understand this and this cleared everything up, thank you
My long await topic!!!! Thank you for posting this PCA lesson
I HAVE SOME QUESTIONS:
1)if you use ur PCA data that has been scaled before doing any train test split...wouldnt it cause Data Lakeage?
2) should not the target be dropped?
I had the same doubt, have you got any update on this ?
Impressed with the clarity of explaination
glad you liked it
Nice Video sir. Please make a video for the math behind PCA. Thanks in Advance Sir
ruclips.net/video/FgakZw6K1QQ/видео.html this should be sufficient if you want to know mathematics
U can get best of pcs by taking log(total features)
I was trying to understand PCA, this video helped me a lot
Hi. I have a question. why when you perform PCA, for input, you did not give (x_scaled), you gave x?????
great explanation on PCA. It's an abstract concept to grasp. well done
Great explanation on PCA. @codebasics: I must have missed it though, but after running the PCA to identify which features will give the results, I missed where one might get the features.
Another very informative video.
DHANYAVAAD ! :)
Your videos have helped me to complete my MSc research. Thank you for being a great teacher. I do have a question, during the explanation, you said we should always use PCA on the scaled data but during implementation, you used the unscaled data. Could you explain this?
I had the same question following through.
I tried using PCA on the scaled data and used it in SVM, Logistic Regression and RandomForest classifier, but the results were almost the same as to using regular data with PCA.
Highest accuracy: SVM - 85.83%, after PCA (3 PCs), accuracy was 83.87%. For all 3 models, accuracy slightly (
I have a query to ask. You said we should pass the data to PCA after scaling but you passed the original X instead of X_scaled. Why ??
Highest accuracy 0.8729 with SVM (linear) and with PCA n_components = 11.
I would add that reducing number of columns should help with overfitting
Thanks for this amazing tutorial. Hope you could do one video about when to use feature selection and feature extraction, or even combination of them.
I am watching this vedio 2nd or 3rd time. @Dhavel you best among equals...👍👍
great looking for more videos dhaval bhai............../
Amazing explanation, I understand PCA now.
After completing the assignment, i got a best score of 0.85 with svm:rbf kernel and after PCA my best score reduced to 0.68 still svm:rbf kernel
Shouldn't X_scaled be passed inside pca.fit_transform() method? Because if you're passing X, there's no point we did the scaling right?
Exactly, i was about to ask the same question.
I tried both. When passing X to pca without scaling, I got higher score. But you're right, I also believe to pass X_scaled for parallel comparison.
Sir can you please tell which features it reduces How can I get those particular selected (reduced) features as output?
Wonderful, as always - thanks for making this video, it has helped me a lot ! Regards
Thank you code basics❤
thanku ..pca explained very easily
@codebasics Thank You for Teaching and helping us!
I am getting around 80 % score when using PCA(0.99999) in exercise, which is higher than the score before using PCA, and also getting a better score without removing outliers.
That’s the way to go kv, good job working on that exercise
Sir also make a video on how PCA actually works and what type of information we can gain from the loadings like how are these features affecting the label
Hi
Good tutorial, i think you must use X_train in pca.fit_transform and not X. Thanks
Thank you for the video. I have one problem, though: Shouldn't we first split the data into training and test sets, then scale each set separately using StandardScaler(), RobustScaler(), etc. ?
Thanks for sharing, it's highly appreciated
Thank you Sir. It was really helpful.
Please also explain the math behind it. You get questions on math behind PCA in interviews. People ask the derivation of PCA
Why didn't you used X_scaled as the parameter to the fit transform function of pca?
Very informative and practical.
How can I get selected features in dataframe along with its feature name instead of number 1, 2, 3,etc.?
Thanks for the explanation. I have one question: When you are doing PCA, you are taking the whole data and after that you are doing train test split. Isn't bit unethical? Again, if I do pca on train data, is same will the same pca can be applied on test data? Is there anyway to store the transformation of PCA to apply that on test data?
I had the same doubt, have you got any update on this ?
Very nicely described ! I have a question:
In your example PCA(0.95) reduces the dimension to 29. But, how do we know which dimensions it picked? I am asking this because I want to use PCA to determine the principal drivers in determining the targets.
hey buddy, PCA doesn't pick any existing dimension. It creates new dimensions which by the naked eye won't make much sense to you.
You kill it, amazing!
You are the best!!! 👏
Thank you 🙏
Thanks sir 😊😊
I have question, we can trained model using pca , then how about imbalance dataset? We not need to do imbalance?
Hi. I might not be making any sense here but I wanted to know if same thing could be achieved with entropy and information gain. We know information gain tells you the feature with the most information or importance as a number . Therefore, in theory, we can remove all the features with really low information gains. I think it would this would work with discrete data better. I don't know if it already exists. If it does, what method does this. If it doesn't, can I know if this solution is practical.
What was the purpose of scaling X at 14:18?
Thankyouu so much for this amazing video
You are absolutely amazing.
sir you told that we should scale before pca but you are applying pca to non scaled data in code
thank you so much
Thank you
Sir please also make a video on how to find relations using descriptive technique.
Great video!
Thanks
Thanks ❤
Can you tell how to do varimax rotation?
Nice video
Thank you
Why PCA rotate the axis? What is a significance of that?
nicely done
How to interpret PCA result in regression?
Thank you so much
Hi Mr. Dhaval, I am so thankful for sharing such a good informative video. Like "ogobuchiokey2978" even i wanted to know, if there is a specific reason of not selecting X_scaled while fitting into PCA? In the above demo, if I fit raw X, I get 29 new PCA-features but if i fit scaled_X i get new 40 PCA-features.
Similarly through your exercise, if I fit scaled_X I get 10 features (only 1 attribute is reduced) with Accuracy of 85% and if i fit raw X, i get 2 attributes, but accuracy dips down to 69%(Random Forest)
I believe this depends on the data as well.
is it X_pca=pca.fit_transform(X) or X_pca=pca.fit_transform(X_scaled)
nice explanation !!
Superb
Nice points.
Hi, I just want to ask why we use StandardScaler instead of MinMaxScaler in the lecture (not exercise). Thank you so much for your help!
The dimensions it should get reduced to, isn't it another hyper parameter? For 2 dimensions yeah you can visualize. For > 2, visualization is not possible. How can you decide what dimension you should reduce your dataset to?
sir i have a question
how one can predict target values when PCA create all new columns based on its own calculations
Hello, I have newly started working on a PCR project. I am stuck at a point and could really use some help...asap
Thanks a lot in advance.
I am working on python. So we have created PCA instance using PCA(0.85) and transformed the input data.
We have run a regression on principal components explaining 85 percent variance(Say N components). Now we have a regression equation in terms of N PCs. We have taken this equation and tried to express it in terms of original variables.
Now, In order to QC the coefficients in terms of original variables, we tried to take the N components(85% variance) and derived the new data back from this, and applied regression on this data hoping that this should give the same coefficients and intercept as in the above derived regression equation.
The issue here is that the coefficients are not matching when we take N components but when we take all the components the coefficients and intercept are matching exactly.
Also, R squared value and the predictions provided by these two equations are exactly same even if the coefficients are not matching
I am soo confused right now as to why this is happening. I might be missing out on the concept of PCA at some point. Any help is greatly appreciated.Thank you!
Why didn't you normalise y(i.e. the dataset.target)?
Should you scale the data before PCA?
I have no idea where to start on the exercise or even what "z-score" means for getting rid of outliers.
Check out video #41 in this video series. That teaches everything about Z Scores.
Hi Sir, very informative video.
I have a problem
I uploaded a local dataset and when I use code
dataset.keys ()
which shows
Index(['Unnamed: 0', 'Flow ID', ............
Now at
pd.DataFrame(dataset.data, columns=dataset.feature_names)
then it shows an error even I changed data into Unnamed as well but ut occurs the same problem.
AttributeError: 'DataFrame' object has no attribute 'data'
waiting for your kind reply.
Thanks.
Please I have a problem with some work.
I was given a list of words let’s say about 200 different words. And I’m meant to create a code that generates 3 random words each together.
Eg wordlist=[a, b, c, d, e,................z]
Output should be = a, d, z
c, o, x
And so on
Please how do I do it?
hello, if I want to visulize this data in 3D, then what will be z axis ?
Thanks v m! can you pls help explain Eigen values and Eigen vectors from the data science perspective? thanks again
hello sir, i am 27 now and masters is in progress
sir i have teaching experience.
but now i want to start my career in Machine learning or data science ?
so it is possible? every company needs new fresh comers so what should i do?
How to see which columns are this?
I have a question. After going through scaling, and then PCA, the features are now all different to the original of values between 0 and 16. When I have a new digit to classify and been provided with the same 64 features, how do I make this new prediction?
You have to scale the new digit and apply PCA to it
before classification
feature_names column is now not reflecting in Load_digits() dataset
amajing
you applied pca on normal dataframe whereas in theory you explained we need to apply on scaled dataframe
17:45 - shouldn't it be pca.fit_tranform(X_scaled) instead of X ?
X is correct, PCA will take care of dimension reduction on the original dataset. However you can also try X_scaled, nothing wrong with it. You may in fact see better results, can you try and please post your findings here?
@@codebasics Using X, I got an Accuracy of 97.22%, using X_scaled, I got a slightly lower accuracy of 96.39% which is interesting. I also tried with "StandardScaler" instead of "MinMaxScaler" and observed this trend.