AdaBoost Algorithm | Code from Scratch

his video guides you through building the AdaBoost algorithm step by step in Python. Perfect for beginners looking to understand and implement AdaBoost on their own.

============================
Do you want to learn from me?
============================

📱 Grow with us:

[Shisuiii]

CampusxBoost(consistency=True,
hard_wrork=100%,
quality = outstanding,
future_Ml = bright)
Salute to you sir ❤

[iyqifvi]

no words, just the best from scratch explanation, I have watched my professors explanation but always get's puzzled how they derived it. you just nailed the in-depth explanation at each step.

[abhasmalguri]

Literally watching your 100th video in this series!
Your content is really informative and addictive!
The journey of learning from Day 1 to now has been full of excitement.
Thank you for this Wonderful contribution to the Data Science community!

[thisisvishalpandey]

Time stamp: 13:31
The training of the third model using the DecisionTreeClassifier contains an error. We should have used the third_df instead of the second_df. It is understandable; such mistakes can occur during code processing. Nevertheless, it is still a great implementation. Thank you.

[sahilkayastha]

100 videos done! Your clear explanations & passion make ML a blast! 🚀🤖🙌

[RahulRaj-nwrr]

There is a slight logical mistake in sir's method of creating a new dataset after upsampling. The correct way is to reset the index of the new dataset after creating it by upsampling. Because if we do not reset the index, then the next dataset will not contain the desired rows. You can check it too. So, each time after creating the new dataset, you need to run this code before processing the new dataset for the next decision stump -

new_df = new_df.reset_index(drop = True)

[anaykhator]

Why do we need to up-sample the dataset if we have already updated the weights of the correctly and incorrectly predicted samples. The incorrect ones would automatically have greater say in the subsequent samples, right? Also, if we up-sample the dataset and create new one in each iteration, few rows of data (correctly classified) will keep on disappearing from the dataset and the dataset will be left only with hand full of values copies of incorrectly classified samples. Wouldn't the diminishing representation of all the rows in each iteration not let the model understand the deeper trends/pattern in the data?

[core]

great sir,
I really enjoyed watching this series.

[abhinavkhandelwal-ww]

Hi Nitish, Thank you for your playlist on Machine Learning. Helping me alot as i am switching my career from Project Management to Data Science.

In this video, in 2nd df you said that row 0 is wrongly classified but it was row 2 as you said at 12:54. But at 13:22 you said 0th row which was wrongly classified. In the upsampling we did't get 2nd row we got only 0.

Need your clarification on this. Hope you will respond.

[AltafAnsari-tfnl]

Hats off to you because i think you are the only one who has shown the algorithm working in a notebook from scratch.

[iamdSachin]

Hello Sir, XGBoost is not included in playlist, could you please make a video on XGBoost ?

[kislaykrishna]

Sir, please upload video on daily basis. As I am following your classes and some blogs to learn data science 🙏

[singh]

while training the third model you have taken values from second model because of which you got too many error value in 3rd training, anyways video was great

[aditya_]

thank u very much sir really nice video

[meenatyagi]

But how can we extend this code for multiclass classification problem as sign test is used for only binary one?

[mustafamarvat]

Are we going to use the updated weights in the second ? Ya phir whi model 1 k weights use kerney hai ?

[rajgurubhosale]

i have a question why u he didnt created a video on adaboostRegressor?

[murumathi]

Hai sir...pls help one question sir.. I have a 1 input apply but my output max 10 ..it's possible sir ....

[aditiarora]

sir can we use the same code for image dataset?

[pradeeptamohanty]

sir there is one doubt if error is more thn 0.5 then alpha is negative .so, it will start decreasing weights of wrong one and increasing weights of right one.