Machine Learning Tutorial Python - 21: Ensemble Learning - Bagging

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?

Why during cross validation using original unscaled X instead of X scaled? Does it not affect accuracy?

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

this was nice and straightforward, and the quip about "copy and paste" was hilarious

It us very helpful video to do my research project.!!!!!

Thanks a lot for your awesome series!

I also learnt that bagging doesn't do so much in increasing the performance of the model apart from lowering the variance.

My results after completing the exercise
svm:
Standalone 0.82
Bagged model 0.87

Decision Trees:
Standalone 0.79
Bagged model 0.84

My bagging model score came out to be : 0.8027, SVC : 0.8804, Decision Tree : 0.804

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 ?

How to train multiple file and then provide them label for individual file and classify a file?

O my gad, my computer get fever for 1 month wkwkwwk. Btw thank you sir for your clear explanation.!!!

Thank you so much sir for this ML playlist

One of the most underrated playlists for ML . I wish lots of student will join ❤

I tried clicking on soultion
Now I have fever

bagging svc gave a far better result than bagging decision tree

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

Thankyou SIR! for this amazing playlist on machine learning

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?

thanks sir

Awesome 🔥
Appreciate your effort bro

Shouldn't you use X_train in the cross-validation calls?

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

good work really

Sir, I don't see any time series forecasting model videos, request to upload videos for the same

Best explanation, EVER!!

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.

thank you very much for this video

when the boosting will be uploaded ?

Your tutorials are not properly structured and are not learner centric!

That was clearly describe what is the bagging method, I wish you had a video about Boosting as well

You have to do outlier detection because the max is much higher than that of 75% value

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).

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?

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.

@ 21: 50 in cross Val score you x and y why not x_train and y_train can anyone explain this

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

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 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:|

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.

I am waiting for Boosting and Xgboost methods sir

Thank you sir for a very good explanation. Those examples are very good to training write a code and cause strong motivation.

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'''

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.

You are the best! Thank you

Random forest explanation is superb 👌