I have a big one question: What is the difference of build a Machine Learning application with Pipeline and to build a machine learning application with a OOP technique? I see that it is the same.
Everything in Python is defined as a class so we use OOP all the time. Pipeline provides a nice flexible way to combine multiple transformers and an estimator.
hello As you have imported polynomial features and transformed the independent variable(X_train) for it be fitted in a polynomial regression then why did you put linearregression() as the estimator in the last tuple of the list?? shouldn't you have use polyfit function or something else? NOTE: I am a beginner here , so the doubts can be silly.
Good question! We have already created all the polynomial terms that we need, i.e., x, x^2, x^3, etc. Thus, we can now view this as a linear regression problem with respect to the "new/artificial" features.
@@DrDataScience one more thing I need to ask if you can spare some time, I have seen people do parameter scaling using StandardScaler() before polynomial features and estimator in a Pipeline argument, so is the scaling a necessary step or we can skip it??
Because each column corresponds to a feature or attribute of your data set. Thus, the number of elements in that column vector is equal to the number of samples.
Short and crisp. Thank you!
that's a great introduction to pipelines! Thanks
Thank you!
thanks for your short, useful introduction!
it helped me a lot
Thanks for the comment.
This is excellent. Thank you
Thanks!
Thank you for this useful video!
Love your tutorials..
Thanks!
muchas gracias saludos desde chile!!
Gracias!
HI there, very happy with this channel could you explain a bit simpler what is pipeline part!
Thanks, its really helpfull
Thanks for watching this video!
I have a big one question: What is the difference of build a Machine Learning application with Pipeline and to build a machine learning application with a OOP technique? I see that it is the same.
Everything in Python is defined as a class so we use OOP all the time. Pipeline provides a nice flexible way to combine multiple transformers and an estimator.
hello
As you have imported polynomial features and transformed the independent variable(X_train) for it be fitted in a polynomial regression then why did you put linearregression() as the estimator in the last tuple of the list?? shouldn't you have use polyfit function or something else?
NOTE: I am a beginner here , so the doubts can be silly.
Good question! We have already created all the polynomial terms that we need, i.e., x, x^2, x^3, etc. Thus, we can now view this as a linear regression problem with respect to the "new/artificial" features.
I appreciate your reply , it cleared exactly what i was asking. Thanks
@@DrDataScience
@@DrDataScience one more thing I need to ask if you can spare some time, I have seen people do parameter scaling using StandardScaler() before polynomial features and estimator in a Pipeline argument, so is the scaling a necessary step or we can skip it??
Great video. Helped me a lot
Why are all arrays converted to column matrices while applying sklearn
Because each column corresponds to a feature or attribute of your data set. Thus, the number of elements in that column vector is equal to the number of samples.
Thank you :=}
You are welcome!