Creating Pipelines Using SKlearn- Machine Learning Tutorial

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Just like school, this guy explained. Crystal clear!

Thanks, man.

kaushikumang

Thanks for the "particular" video

mauricioalfaro

Could you please explain the process of creating pipelines when we have custom methods like a method to deal with missing values, another method to drop highly correlated features etc.. instead of sklearn methods like StandardScalar() and PCA()

benvelloor

Excellent video! Thank you for your contribution. As an aspiring data scientist, your content is helping me greatly.

jonathanmaravilla

You're such an amazing teacher! Thank you so much for all your work, you have no idea how much you've helped me!

tessdejaeghere

hi Krish, Pipeline is good concept and if numeric features it is just cake walk ..but if text classification or any other how it can be while doing predictions ie once you dump model using joblib then how you save integrity of data while predictions i mean ..1)get input & show result???

reddyvarinaresh

Krish,
I have some doubts pls clarify:
1.In pipelines, scaler can be used only when all features in dataset are numeric. Right?
2. When you execute the command pipe.fit, how does Pipeline know to perform scaling only on X_train and not on y_train. Also, how does PCA() know only to take X_train and find pca components.
Does it recognize X_train and y_train based on ordering in the pipe.fit() command?

madhuful

Hi there. Thanks for the tutorial. One thing... Using the make_pipeline function to create pipelines is marginally better, or user-friendly, than using the Pipeline object directly.

dariuszspiewak

this video on pipeline is super helpful to my project. thanks!

pookpratch

Please have my "like".
Thank you for this video, the notebook presentation and the explanation are both very clean and neat. It is intellectually soothing to come across this kind of content.

TheIstNE

Hi Krish, Super helpful video... THANK YOU!

amirtaghavy

Hi Krish Thanks for the video.. Can you please advise if we can also use this for removing the duplicates and other preprocessing and data cleaning activities. If yes can you please make a video to show that.

arvindchandrasekaran

Great ! I have some project needs to find developer working remotely in India.

zhang

Nicey explained and pipelines are very useful.

mannudhapola

@krish Why do Decision Tree and random forest also have the same accuracy?

gopalkrishnahegde

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

aakaashskale

Thanks mate, good video. cheers from belgium

perkelele

it is same as a function we create,
but pipeline store permanently in our sklearn library

akashpawar

I think doing Standard Scaling for decision tree and random forest classification was not mandatory, right?

arjyabasu

please can you do a video on Custom Transformers..you are amazing man!!!!

chowadagod

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