วีดีโอ

you look like brad pitt, awseome video

Really like your all the videos, I always look for videos where things are explained with examples, as well as implementation from scratch. And All your videos fulfils my expectations. Thank you

thank you friend

pessimistic vs optimistic pruning?

Phenomenal job ❤❤❤❤❤

جزاکم اللہ خیرا بھائی

I am getting error 'dict' is not callable when calling getbestsplit. How do i fix that

Thanks for the tutorial finally I understand pre-pruning and post-pruning!

Thank you so much for this excellent series! This helps me a lot.

thank you you are the best teacher i have ever seen as i take long time to understand it but i got it with you clearly so don't stop please ❤

Why we use sigmoid? Isnt on the multi class problem we use softmax? Please answer please i really confusseeee

Wonderful video, thank you so much. I tried to do decision tree from scratch from myself and faced several issues, then I watched some TH-cam videos and those were too complicated. I gave up on coding decision tree by myself. but you made it super easy, the way you explained. I am extremely surprised why this video has only 146 likes, it deserves millions. Thanks a ton for your efforts!

can you also provide us this sample data for decision tree

looks like there is no bias, how to do back-propagation of bias values ?

Can only agree with what has been said so far. Such good content and easy to grasp explanations.

Thank you so much, your contents deserve much more attention!

exactly what i need! thank you

Clarification on Information Gain vs Overall Entropy (17:45) The formula for Information Gain is basically like this: Information Gain = Entropy before split - weighted Entropy after split Or in other words: Information Gain = Entropy before split - Overall Entropy So, to determine the Entropy before the split, we need to calculate the following: Entropy before split = 42/130 * (-log2*42/130) + 42/130 * (-log2*42/130) + 46/130 * (-log2*46/130) = 1.584 So, the Information Gain for split 1 is: Information Gain = 1.584 - 0.646 = 0.938 And the Information Gain for split 2 is: Information Gain = 1.584 - 0.804 = 0.780 So, split 1 results in a higher Information Gain and we would chose it over split 2. Therefore, we get the same result compared to the video, where we just used Overall Entropy. The reason I decided to just use Overall Entropy and not Information Gain is because they are essentially the same. With Overall Entropy you focus on the fact that the entropy decreases from 1.584 to 0.646 after the split. And with Information Gain you focus on the fact that the entropy of 1.584 decreases by 0.646 to get an Information Gain of 0.938 after the split. In my opinion, using Overall Entropy is simply more intuitive. Additionally, it requires one less calculational step.

:/ hey bro it seems like information gain and overall gain causes different split condition. If I use information gain the tree matches with sklearns Decision Tree algorithm (entropy).

Great explanation!

is this id3 algorithm?

This is so helpful, thank you for making this!

It's seem so simple when you do it, but for me need hours to understand your work so your work is truly impressive

hi can anyone tell, where has that variable "overall_entropy=999" has come from at 20:35 ???? plz

This videos are not famous enough. So good.

switch to dark mode powerpoints.

thanks a lot for those videos!! these seriously just saved my grades in data science!!

could you pls explain what type of pruning is it i.e. is it cost complexity pruning like in CART or something another and why did you decide to use this method?

Overall_metric might be used as np.inf against True/False with 1'st iteration. In my ipinion it looks clearer on an intuitive level.

May I ask a question: why did u split data taken the average between two unique values? Is it a rule or may I split data just have been taking every unique value? I thought that in this case we shouldn't care about type of value (categorical and so on).

Thank u for a great tutorial. Is it a CART version?

Hi Sebastian, thanks for creating such useful videos. I'm also wondering what I should do if I would like to know the number of observations and entropy value of each leaf node. Thank you!

Hats off to you sir! Thank you for such a deep and detailed video for post pruning a decision tree!!

Is this classification or regression ?

Hi Sebastian Great job! Listen, I tried your calculate_r_squared function () but there seems to be an error Basicly, python does not like your statement grid_search = pd.DataFrame() It's only when I set the grid_search name to df that the error vanishes, however then I have to set a loop for printing - in this case I write if max_depth = 6 then print(df) and I get a list of 17 items. [see below] This is not very elegant but works after all. Can you please explain explain why I get this error? Many thanks ViG df = pd.DataFrame(grid_search) df.sort_values("r_squared_val", ascending=False).head() dfs = df.groupby('max_depth') dfs_updated = [ ] for _, df in dfs: df['id'] = df.groupby(['max_depth','min_samples', 'r_squared_train', 'r_squared_val']).ngroup()+1 dfs_updated.append(df) df_new = pd.concat(dfs_updated) df_new if max_depth == 6: print(f"") print(df_new)

hidden gem!

Decision Tree is working fine with small dataset. Its not finishing a binary classification for data with 3 Lakhs rows. How can we reduce the iterations in determine_ best_split() ??

How to open a jupyter notebook from conda environment

why you stop making videos ,your videos is outstanding

i have watched whole series on Basics of Deep Learning Part 1-15: Coding a Neural Network from Scratch in Python......got hooked to it and completed on binge.

This is indeed unique style from the entire youtube i've ever watched.Excellent OO methods.

I'm lucky to find your videos..Thanks for this effort :)

SUPERB EXPLANATION! THANK YOU!

What a great tutorial. But how can I visualize the random forest by plot?

Awesome video . I have a school assignment and your videos are a literal life saver. Am trying to use your code for a multiple split classification problem that im working on now

Really nice one! :D

great video, i always wondered how decision trees are taking splits. like ifunique values are 2,3,4. does it split lik. e < and > 2.5,3.5 or does it use < & > 3,4 like that. are you doing it in same way like in sklearn ?

Hey Sebstian,you’re an amazing teacher thank you for sharing this informations with us , please can you help me ? I have a question ,how can we handle categorical columns that have many unique features ? I have a columns named « country « that have 174 unique value I want to use it to solve a classification problem using a decision tree please ,if a use on hot encoder I get too many columns and my tree became very big I can’t even see it how can I solve this problem please

Sir, could you please make a visual representation of the tree, it seems good when it is interactive.