## 3.10 Training logistic regression with Scikit-Learn

<a href="https://www.youtube.com/watch?v=hae_jXe2fN0&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR"><img src="images/thumbnail-3-10.jpg"></a>

[Slides](https://www.slideshare.net/AlexeyGrigorev/ml-zoomcamp-3-machine-learning-for-classification)


## Notes

This video was about training a logistic regression model with Scikit-Learn, applying it to the validation dataset, and calculating its accuracy. 

**Classes, functions, and methods:** 

* `LogisticRegression().fit(x)` - Scikit-Learn class for training the logistic regression model. 
* `LogisticRegression().coef_[0]` - return the coefficients or weights of the LR model
* `LogisticRegression().intercept_[0]` - return the bias or intercept of the LR model
* `LogisticRegression().predict[x]` - make predictions on the x dataset 
* `LogisticRegression().predict_proba[x]` - make predictions on the x dataset by returning two columns with their probabilities for the two categories - soft predictions 

The entire code of this project is available in [this jupyter notebook](https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/03-classification/notebook.ipynb).

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* [Notes from Peter Ernicke](https://knowmledge.com/2023/09/30/ml-zoomcamp-2023-machine-learning-for-classification-part-10/)

## Navigation

* [Machine Learning Zoomcamp course](../)
* [Session 3: Machine Learning for Classification](./)
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