MultiClass Logistic Regression, Python Machine Learning, Detecting multi outcome logistic regression
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- เผยแพร่เมื่อ 21 ธ.ค. 2024
- In this #PythonMachineLearning series, #MultiClassLogisticRegression is explained step by step using #IRISDataset. Logistic regression is applied on iris dataset and the species of iris flower is detected using its #PetalLength, #PetalWidth, #SepalLength, #SepalWidth.
Link to the code:
github.com/kjs...
Link to Iris dataset used:
www.kaggle.com...
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Very good approach... One thing could have be applied here for converting Species into numeric data is, LabelEncoder. It will be flexible for any number of species.
lovely content
Thank you for this video. It helps me a lot with my project.
The best tutorial thanks!
very nicely explained.
How to extract sigmoid function from it
Thank you for this tutorial, it's very clear!
I have a question on the final part of the video, for the same model, with a test size of 0.3, how we improve the score of 0.933, what do we have to tweak?
i think normalizing/scaling the data and hyper parameter tuning may help
Thank you!
Can you make video without using sklearn. Using sklearn a baby would make a model. A video should have a proper knowledge that gradient with batch etc.
And BTW you should not label y as 1,2,3 but 0,1,2 to proper analyze the confusion matriz
Use label encoder.
Wrong. U don't use score in logistic regressions. U use accuracy.
Don´t use replace its a bad praticse. Use catergory instead:
df['Species']=df['Species'].astype('category')
df['Species']=df['Species'].cat.codes
df