# Tag Info

8

Logistic regression corresponds to a Binomial distribution, a member of the exponential family, so in that sense it is nested within the Exponential Dispersion class of models. The $\mathrm{Tweedie}(\mu, \sigma^2)$ family specifically is also contained within $\mathrm{ED}$, but imposes the mean-variance relationship \begin{align*} \mu &= \mathbf{E}[Y]\\ \...

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No. A GLM is characterized by its link function and its target distribution. TweedieRegressor assumes a Tweedie distribution for the latter, which do not include the Bernoulli distribution needed for logistic regression.

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i can only answer this question in a confirmatory way. Normally, many ppl in ML have huge datasets with lots of features and rows or only a few features from kaggle with a moderate amount of rows. What is common to most ppl regardless of the datatset is, they dont derive a hypothesis or work out material. They see it as an EDA and want to confirm their ...

2

This is sometimes referred to as estimating a "learning curve". However the code you have provided has several shortcomings, which I believe makes the results not so useful. Hyperparameter estimation is done on the same set as the final performance evaluation. This not a valid estimate for performance on unseen data. The model may be overfitted to ...

2

The conditions you quote are for training: SVM attempts to find $w$ and $b$ such that $$y_i (w \cdot x_i + b) \ge 1$$ where $y_i \in \{-1, 1\}$. However, this can only succeed if the classes are linearly separable. If not, as your case suggests, you need a soft margin SVM, by introducing slack variables: $$y_i (w \cdot x_i + b) \ge 1 - \xi_i$$ For ...

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you are fitting your whole data to it, it seems the cross_val_score awaits a predefined train_test_split object. Ignore the part with 'bla' :-). Btw. it is also the kaggle data. I used it in one of my own notebooks.

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I agree with Patrick's comments above. I found the following articles useful which highlight that removing independent variables related to multicollinearity will improve the model output and this can be performed using a loop. https://beckmw.wordpress.com/2013/02/05/collinearity-and-stepwise-vif-selection/ Why is multicollinearity not checked in modern ...

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To test my model should I split my data into 3: training, validation, test? If you have sufficient data to do so, then this might be reasonable. I think there may be some arguments to be made that cross validation is preferable to a one time data splitting. See Frank Harrell's Regression Modelling Strategies chapter. 5.3.4 for more if you are interested ...

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