Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7

In logistic regression, polynomial features involve transforming the input features into higher-degree polynomials. This helps capture more complex relationships between variables, potentially improving the model's ability to fit non-linear patterns in the data. Polynomial features are a powerful tool in logistic regression for handling intricate decision boundaries and enhancing predictive performance.

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[hossain]

Is Onehotcoding included in multinational class by scikit learn?

[GovindSingh-grcm]

plot_decision_regions is not working on minist if I have more than 2 components. I am using 35 as it gives the best accuracy.

[TishaGupta-fd]

Please share the csv file also for this code

[LetsClarifyIt]

One doubt sir, if we have 5 features and only one features in non linear than we need to apply this polynomial feature to only one column right

[ahsanzafar]

How to plot decision boudaries for models that are trained with more than 2 features? In all videos you always used 2 features and plot decision boundary, how to plot for models with more than 2 features?

[yuvistrange]

In polynomial logistic regression with 2 input columns, applying a degree of 2 generates 6 features.
This includes:
1. A constant term (intercept): x1^0 and x2^0 combined
2. Linear terms: x1 and x2
3. Quadratic terms: x1^2, x2^2, and the interaction term x1x2