Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)
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- เผยแพร่เมื่อ 15 มิ.ย. 2024
- Logistic regression is used for classification problems in machine learning. This tutorial will show you how to use sklearn logisticregression class to solve binary classification problem to predict if a customer would buy a life insurance. At the end we have an interesting exercise for you to solve.
Usually there are two types of machine learning problems (1) Linear regression where prediction value is continuous (2) Classification where predicted value is categorical. Logistic regression is used for classification problems mainly.
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Code: github.com/codebasics/py/blob...
Exercise: Open above notebook from github and go to the end.
Exercise solution: github.com/codebasics/py/blob...
Topics that are covered in this Video:
0:00 - Theory (Explain difference between logic regression and classification)
1:18 - What is logistic regression?
1:26 - Classification types (Binary vs multiclass classification)
1:53 - Explanation of logistic regression using the example of if person will buy insurance based on his age
5:38 - Sigmoid or Logit function
8:18 - Coding (for coding we are using an example of if a person will buy insurance or not based on his age)
14:36 - sklearn predict_proba() function
15:49 - Exercise (Solve a problem of predicting employee retention based on salary, distance to work, promotion, department etc)
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I have never seen any other video explaining the concepts of machine learning so clearly. Keep up the great work..!!
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I really appreciate your tutorial videos, thank you. But how to find the "bought_insurance" values ?
One of the few videos that clearly shows the training data that the model is attempting to fit to. Thank you.
Perfectly balanced video. It forces anyone to continue to watch other videos of this series. Very well explained in simple language. 👌
Solution link for the exercise: github.com/codebasics/py/blob/master/ML/7_logistic_reg/Exercise/7_logistic_regression_exercise.ipynb
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Glad you liked it :)
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Thank you again! Great explanation! Always great tutorials!
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I am glad it was helpful. :)
Thank You Sir, I have learned a lot from your vids :). I was really perplexed by Logistic Regression and I am glad
TH-cam recommended this to me :)
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This video is fantastic. I'm teaching myself machine learning and this was one of the most helpful resources I've found online. Excited to watch/work-through the rest of the videos! Thank you so much
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Others making complicated like maths intuition.
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Hi, I have some slight problem. How can I plot the prediction curve after training my model? Would be glad if you reply. Thanks
Could you pls tell me what exactly did you do to fine tune it?
it is one of the fantastic videos about Logistic Regression .. Many thanks
Detailed and really helpful. Thank you.
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Perfect explanation on logistic regression.
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Glad you liked it
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Glad you like them Riya and I wish you all the best! I have many playlists and recently left my job to focus on online teaching. My goal is to produce even a better quality tutorials then this.
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@@codebasics One question here. Why we did not remove one of the dummy variable after dropping salary column in Logistic regression like we did for Linear?
@@riyamitra8901 I think ...as logistic regression can handle multicollinearity between the dummy variables so it's not necessary to drop the last col.
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Glad it was helpful!
clearly understandable explanation.
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thank you sir for your video.
Thanks a lot for this i was able to implement logistic regression after so many tutorials
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Very interesting and useful - well presented too
This video had good information, it was really helpful. I am still a learner, new to this field. I understand how to write and basics of confusion matrix using binary classification. But some terminologies are confusing. Can you please explain what exactly are base rate, test incidence, conditional incidence, classification incidence? That would be appreciated.
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78 percent accuracy. I do all your exercises but in this I learned a lot. Thank you sir for such a great series @codebasics
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Now What are you doing. I mean study or work
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Thanks a lot for the lucid explanation.
In the exercise, I got an accuracy of 77.2% in my model prediction.
Hi bro...
Now I learn machine learning...
What are you doing.... I mean study or work
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Sparsh
Thanks for appreciation Sparsh. I am continuing the series, it is just that due to my schedule I am not finding lot of time to work on it but I will try my best to speed up new tutorial additions.
On first attempt, i considered 'left' as dependent variable and everything else including salary and department as independent variable, got 77% score of accuracy. Thanks for the wonderful video.
Great job manu. its a good score. Video description has a solution link, you can verify your code with mine.
Finally got the Python version of Andrew Ag's machine learning course. With a better explanation.
thanks.
😊👍
amazing tutorial!
Well explained, Thanks!
Bro it was easy and clean. Thanks!
Dear Sir
What a beautiful datasheet you have provided for practice with this video.
Spent more than two days to play with it.
Playing with the datasheet opened another dimension of the learning curve.
Thank you very much for providing relevant exercises like this as a challenge!
Happy that this is helping you Nilupul.
Thank you, now if you have many features in your binary classification problem, and the classes overlap from visualizing data using pca, is it advisable to use Logistic Regression in this case?
Thank u so much..it really helped to clear my concepts
You are the best teacher
You guys are life savers. man love your videos.
Amanullah, I am happy it helped you :)
Many thanks this is the first explanation that provides context and examples making its so simple to understand.
Glad you liked it Michael
@@codebasics 15:35 I can’t execute it??
model.predict(57) and any number like 25, 60 got the following ValueError: Expected 2D array, got 1D array instead:
array=[57].
it was amazing. thank you so much
Awesome as always, thanks for everything!
i got a 77% model accuracy based on the satisfaction_level
How did you get the prediction model accuracy by depedent variable? And 77% meaning is the probability that they will leave the company?
@@jsbean8415 model.score()
@@nxbil2397 that will show you the overal accuracy of your model. My question is , how you will get the probablity % that the employee will leave given the dependent variables? Like the one you have mentioned "satisfaction level".
Thank you! So well done
Actually you are the best explainer
Thank you so much, sir. I've got the score in the exercise 0.797. 🙂
thank you for the content, helped me a lot!
Sir Kindly confirm whether you already have a video for how to do Exploratory data analysis and feature selection. Thank you.
Keşke türkçe altyazı da ekleseydiniz çok mutlu olurduk.Yine de elinize sağlık.
one question though in the exercise given of employee retention why did not you dummify the dept variable and included in the features, cannot it be one of the important features which we should be including coz I have included it and got better score as well.
Very well explained, thanks!
Glad you enjoyed it.
excellent explanation...thanks lot.. please make video on deep learning model using tensorflow and caffe...
Sir I tried this method, it is very easy to understand and use.. thank you sir
Dhavel you are excellent in explaining difficult concepts in very simple language!
I am happy this was helpful to you.
Thankyou very much for all help and support. Can you please make a video on mathematical explanation of sigmoid and logit function also ?
Suppose our target variable is in the form of categorical such as Yes & No. Then do we need to convert it as 1 & 0? Also, can we have a mixture of independent variable as categorical(string) and continous(numerical)?
awesome.... :) Sir, could you please make a video on how to detect and handle/dealing with outliers in model..? eagerly awaiting from you, haven't got any clarity on outliers.
Thank you very much! Very nice video and perfectly explained!!
Glad it was helpful!
Building model means statistic formula will be the same but we use different columns of Data Frame for an output. Is it right sir?
Nice Sir, try to create SVM or PCA next with some mathematical explanation. thank you
Thank you so much....very good explanation
Glad it was helpful!
Perfect explanation!
Glad it was helpful!
Thank you sir for this amazing explanation of Logistic Regression.
Glad you liked it
Very helpful!
Awesome tutorial
I have a question, how is the slope value updated for logistic regression when finding the best fit plane or line to seperate the 2 classes?
At first I thought gradient descent but that doesn't work because it is used to find the global minima
Maaeri aapahi hans di
Maaeri aapahi rondi
Maaeri yaad yaad wo aaeri
very useful video.... you explain everything in a very simple manner. Thank you
Glad it was helpful!
Sir .....I cannot thank you enough!!!......thanks for introducing kaggle as well.
Thanks narsinha