Thank you so much sir for this ML playlist. Your explanations are simple, exact, and extremely easy to follow. The method that you use of first familiarizing us with theory, then with a practical example and then an exercise is really effective. Looking forward to more of such videos in your ML series. Thanks once again, sir.
This exercise was a challenge. Thank you. By just taking pure z of the set, some outliers were missed. Basically, all the outliers were the 0s for blood pressure and cholesterol. With those eliminated, I got significantly higher scores than the solution. All bagged models gave a similar 86% accuracy. The biggest jump from non-bagged model to bagged model was the Decision Tree which went from 79% accuracy without bagging to 86% with bagging. Also, I did the exercise several months after this video (was posted - not sure when it was made), so the libraries (especially SVC) may have improved (in their defaults).
Your tutorial series are teaching me a lot Sir. These are such well organized. You have made these so easier to learn and understand. Hats off to your hard work. A blind follower of you, Sir. Loads of love.
Thank you so much Sir for teaching us a lot of things. I was searching for here and there for Ensemble learning and you video just showed up. You are life saver. Thanks a lot!!
Thumbs up! You cannot learn swimming by seeing. Who takes the pain of providing excercise. When i was trying to learn in the beginning this was what i wanted. But atleast good some1 is providing it now.
Hi Dhaval! Simple & useful explanation as always. Keep doing more videos. However, @11:40 I believe we have to first do train test split & then we should perform standard scaling operation instead of doing the other way. Aren't we running into the problem of data leakage if we do standard scaling on all the data points without train & test split? Let me know your thoughts. Thanks!
My results of the exercise: svm standalone 0.8, after bagging 0.8, Decision Tree standalone 0.65, after bagging 0.79. Bagging helps improve accuracy and reduce overfitting, especially in models that have high variance. Mostly used for unstable models like Decision Trees
why are we fitting our model on X,y then what is the use of x_train and y_train and no use of scaling also if we are trainning our model on original X and y ?
You can have as many classes as your problem requires in your DNN or NN. Here’s a playlist from code basics for deep learning projects, check out the potato diseases classification course: ruclips.net/p/PLeo1K3hjS3ut2o1ay5Dqh-r1kq6ZU8W0M
Thank you for this wonderful explanation. I have a query here. We scaled X but everywhere we use X in cross_val_score. Could you please explain why we scaled X?
I think in the case of this dataset it doesn't matter much. Scaling was applied just in case and in cross_val_score for both X and X_scaled the results are very similar for different models. But yes. More logical to use X_scaled later.
Excellent expanation sir.The whole series has been exceptional. Had one query -'How can reduction the size of data set decreasee variance .Decreasing no of features might decrease it,but how decreasing no of training examples can decrease it
Hi, can you explain further the difference between bagging and bagged trees. I don't really understand the explanation in the video. Thank you so much for your help! Your videos are amazing.
Using standalone model I got a better score than if I used SVC with bagging: 0.902 versus 0.851 Using standalone decision tree I got 0.782 versus 0.84 with bagging. Bagging helps reduce the overfitting (high variance) caused by decision trees by averaging multiple decision trees.
Hi, Dhaval I hope you are doing well, I have a query in this, at step 35 you have provided input as X and y to the model. What if you have provided input as X_scaled instead of X, i think accuracy might be different.
Sir is there any chance of getting job into ds field who have no coding experience and have exp in non it field i.e electrical field? Please help me out sir?
@@codebasicssorry I have not say I been 10 years in electrical field due to financial crisis when I was passout sir so but in carona time I have been learn pthhon for ds by using pandas ,matplotlib,scipy,skit libraries
Good presentation and preparation; easy to understand. I wish to get a clarification that, why the term "resampling with replacement" is used instead of "sampling with replacement". Is there incidental or there is any specific reason? Thank you.
Thank you for your courses I have differen code to detsct otluiers. This code also works very good. It is more simple. Best Regards '''Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 outlier_condition = ((df < (Q1 - 1.5 * IQR)) | (df > (Q3 +1.5 * IQR))) df3 = df[~outlier_condition.any(axis=1)] df3.shape'''
Thankyou for making such a clear video in bagging and RF. I have one doubt in RF, whe RF does rows and feature sampling so in feature sampling, some of the DT might not get relevant features or not even the features we might wanna use, so doest this affect accuracy and not let us get the result that we want. Ps i know this is a lot of writing!!!!!
Hi sir can make a video on how to combine classifiers like decision tree, random forest ,naive bayes and svm and get a colleciive result, like a weighted output
I dont know why i am getting output and mean as 1 while using DesicionTreeClassifier and RandomForestClassifier, I have tried with different values but value is same and not getting the exact reason. Can you guys tell me where i have made mistake:|
SVC score without bagging 0.87 DecisionTreeClassifier score without bagging 0.76 SVC score with bagging 0.867 DecisionTreeClassifier score with bagging 1.0 Drastic improvement in Decision Tree Classifier
Im not able to see top row, all column headings in names in CSV file downloaded from kaggle - pima-indians-diabetes.csv Am I doing any mistake while downloading?
![I.N "HALLUCINATION" | [Stray Kids : SKZ-PLAYER]](http://i.ytimg.com/vi/n5B5q1Hwt_U/mqdefault.jpg)
Check out our premium machine learning course with 2 Industry projects: codebasics.io/courses/machine-learning-for-data-science-beginners-to-advanced
This channel is golden, i really like how you explain the concepts until execution to practical coding.
so far this is the best explanation on bagging technique I found on RUclips! Great Work
this was nice and straightforward, and the quip about "copy and paste" was hilarious
Thank you so much sir for this ML playlist. Your explanations are simple, exact, and extremely easy to follow. The method that you use of first familiarizing us with theory, then with a practical example and then an exercise is really effective. Looking forward to more of such videos in your ML series. Thanks once again, sir.
This exercise was a challenge. Thank you. By just taking pure z of the set, some outliers were missed. Basically, all the outliers were the 0s for blood pressure and cholesterol. With those eliminated, I got significantly higher scores than the solution. All bagged models gave a similar 86% accuracy. The biggest jump from non-bagged model to bagged model was the Decision Tree which went from 79% accuracy without bagging to 86% with bagging. Also, I did the exercise several months after this video (was posted - not sure when it was made), so the libraries (especially SVC) may have improved (in their defaults).
Your tutorial series are teaching me a lot Sir. These are such well organized. You have made these so easier to learn and understand. Hats off to your hard work. A blind follower of you, Sir. Loads of love.
Thanks for your kind words Khan shian 🙏
One of the most underrated playlists for ML . I wish lots of student will join ❤
Thank you so much Sir for teaching us a lot of things. I was searching for here and there for Ensemble learning and you video just showed up. You are life saver. Thanks a lot!!
Thumbs up! You cannot learn swimming by seeing.
Who takes the pain of providing excercise. When i was trying to learn in the beginning this was what i wanted. But atleast good some1 is providing it now.
The most helpful video for bagging , thank you
Explanation is very easy...well understood....👍👍👍
That was clearly describe what is the bagging method, I wish you had a video about Boosting as well
Thanks a lot for providing very necessary and important contents!
Best explanation, EVER!!
Glad you think so!
great video, best video on the topic
Just as expected...awesome!!!
Hi Dhaval! Simple & useful explanation as always. Keep doing more videos.
However, @11:40 I believe we have to first do train test split & then we should perform standard scaling operation instead of doing the other way. Aren't we running into the problem of data leakage if we do standard scaling on all the data points without train & test split? Let me know your thoughts. Thanks!
Hey yes the dataset that I showed at the top is actually training dataset. I think I mentioned that in video.
Thankyou SIR! for this amazing playlist on machine learning
Thanks a lot for your awesome series!
My results of the exercise: svm standalone 0.8, after bagging 0.8, Decision Tree standalone 0.65, after bagging 0.79. Bagging helps improve accuracy and reduce overfitting, especially in models that have high variance. Mostly used for unstable models like Decision Trees
Random forest explanation is superb 👌
Glad it was helpful!
Thank you sir for a very good explanation. Those examples are very good to training write a code and cause strong motivation.
why are we fitting our model on X,y
then what is the use of x_train and y_train and no use of scaling also if we are trainning our model on original X and y ?
Is there any tutorial on multi class classification with Deep learning "NLP" (Keras)
You can have as many classes as your problem requires in your DNN or NN. Here’s a playlist from code basics for deep learning projects, check out the potato diseases classification course: ruclips.net/p/PLeo1K3hjS3ut2o1ay5Dqh-r1kq6ZU8W0M
Why during cross validation using original unscaled X instead of X scaled? Does it not affect accuracy?
Thank you so much sir for this ML playlist
Thank you for this wonderful explanation. I have a query here. We scaled X but everywhere we use X in cross_val_score. Could you please explain why we scaled X?
I think in the case of this dataset it doesn't matter much. Scaling was applied just in case and in cross_val_score for both X and X_scaled the results are very similar for different models. But yes. More logical to use X_scaled later.
It us very helpful video to do my research project.!!!!!
Awesome 🔥
Appreciate your effort bro
Excellent expanation sir.The whole series has been exceptional.
Had one query -'How can reduction the size of data set decreasee variance .Decreasing no of features might decrease it,but how decreasing no of training examples can decrease it
Concept cleared 🥰😍😍
Hi, can you explain further the difference between bagging and bagged trees. I don't really understand the explanation in the video. Thank you so much for your help! Your videos are amazing.
You are the best! Thank you
You have to do outlier detection because the max is much higher than that of 75% value
Using standalone model I got a better score than if I used SVC with bagging: 0.902 versus 0.851
Using standalone decision tree I got 0.782 versus 0.84 with bagging.
Bagging helps reduce the overfitting (high variance) caused by decision trees by averaging multiple decision trees.
How to train multiple file and then provide them label for individual file and classify a file?
Sir, I don't see any time series forecasting model videos, request to upload videos for the same
good work really
Thanks for the tutorial
Shouldn't you use X_train in the cross-validation calls?
Where can I find the Standard Scalar explanation?
Thanks code basics
when the boosting will be uploaded ?
Don't we need to write code from scratch ??? Simply using sklearn libraries is enough for a ds????
Hi, Dhaval I hope you are doing well, I have a query in this, at step 35 you have provided input as X and y to the model. What if you have provided input as X_scaled instead of X, i think accuracy might be different.
@codebasics can you please tell us, if we can use X_scaled instead of X in our models.
Your videos are great... More power to you...
shukran 4 the entire pl
thank you very much for this video
Sir, i clicked the link without trying the exercise, my laptop is coughing right now, what can i do sir, nowww😢😢😢
Sir is there any chance of getting job into ds field who have no coding experience and have exp in non it field i.e electrical field? Please help me out sir?
Yes it is possible. But if course you need to learn coding and other skills to pass an interview
@@codebasicssorry I have not say I been 10 years in electrical field due to financial crisis when I was passout sir so but in carona time I have been learn pthhon for ds by using pandas ,matplotlib,scipy,skit libraries
Sir..can you explain why you are using StandardScaler method
its used for scaling down the data, for ex 0 to 400 >>>>> -2 to 2 in between this range. its about features having high diff
Dear @codebasics We would be very grateful if you could teach Stacking tutorial please.
Point noted
Good presentation and preparation; easy to understand. I wish to get a clarification that, why the term "resampling with replacement" is used instead of "sampling with replacement". Is there incidental or there is any specific reason? Thank you.
Thank you for your courses
I have differen code to detsct otluiers. This code also works very good. It is more simple.
Best Regards
'''Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1
outlier_condition = ((df < (Q1 - 1.5 * IQR)) | (df > (Q3 +1.5 * IQR)))
df3 = df[~outlier_condition.any(axis=1)]
df3.shape'''
Waiting for your NLP series .
Please Please make it.
Thankyou for making such a clear video in bagging and RF.
I have one doubt in RF, whe RF does rows and feature sampling so in feature sampling, some of the DT might not get relevant features or
not even the features we might wanna use, so doest this affect accuracy and not let us get the result that we want.
Ps i know this is a lot of writing!!!!!
I have a question related to K fold cross validation, how do we select the optimize K value for the cross validation technique?
It's selected arbitrarily. Mostly 5 and 10 are mostly used. The larger the number of K the higher computational expensive the process gets.
Sir can you please share the Machine learning playlist which starts from tutorial 1 . I am not bale to figure the previous tutorials
ruclips.net/p/PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw
@ 21: 50 in cross Val score you x and y why not x_train and y_train can anyone explain this
Can we get update on your DL series ?
by using the df. describe() how can we decide is it necessary to do outlier removal or not please can anyone help me for my question
sir, why most of the example you give is based on classification .. please give us some example on regression problems..
I also learnt that bagging doesn't do so much in increasing the performance of the model apart from lowering the variance.
Hi sir can make a video on how to combine classifiers like decision tree, random forest ,naive bayes and svm and get a colleciive result, like a weighted output
I dont know why i am getting output and mean as 1 while using DesicionTreeClassifier and RandomForestClassifier,
I have tried with different values but value is same and not getting the exact reason.
Can you guys tell me where i have made mistake:|
Sir,I have completed my bachelor's degree .so next which course is better for programming and coding .can you explain me .
How to decide on classifier or regressor after data split ?
It depends on your target value if it is continuous use regression, if it is categorical use classification
Sir i want to do java certification, can you plz help me sir. I have zero percent knowledge sir. From basics.
thanks sir
My results after completing the exercise
svm:
Standalone 0.82
Bagged model 0.87
Decision Trees:
Standalone 0.79
Bagged model 0.84
I am waiting for Boosting and Xgboost methods sir
base_estimator is renamed as estimator.
THANKS MAN
I have zero percent knowledge in software side sir
Can u plz help me sir
I hope you see my questions you never response to my questions. why you didi not fit "BaggingClassifier' with '(x_train,y_train)', in exercise?
👍👍
Sir, u simply use logistics regression
Sir can you do an video for java sir plz sir
My bagging model score came out to be : 0.8027, SVC : 0.8804, Decision Tree : 0.804
SVC score without bagging 0.87
DecisionTreeClassifier score without bagging 0.76
SVC score with bagging 0.867
DecisionTreeClassifier score with bagging 1.0
Drastic improvement in Decision Tree Classifier
How are you using SVC with bagging. Can you send the code of that?
12:04
9:37
bagging svc gave a far better result than bagging decision tree
O my gad, my computer get fever for 1 month wkwkwwk. Btw thank you sir for your clear explanation.!!!
Sir...
Could you please upload a video about Boosting technique
Yes that one is coming next
Most important tool for programmers *copy and paste* 👌 😂😂
I hope you’re feeling better I remember you saying you were sick inshallah
I am healthy as a horse my friend. Thanks for your care and concern 🙏
Who else finished the course?! Like if you did! Me too Me too
I tried clicking on soultion
Now I have fever
i am lost
No worries start again 😊
Your tutorials are not properly structured and are not learner centric!
Im not able to see top row, all column headings in names in CSV file downloaded from kaggle - pima-indians-diabetes.csv
Am I doing any mistake while downloading?