stratify | MongoDB app for building a consolidated timeline | Application Framework library

How to create a list with the y-axis labels of a TreeExplainer shap chart?

Hello,

I was able to generate a chart that sorts my variables by order of importance on the y-axis. It is an impotant solution to visualize in graph form, but now I need to extract the list of ordered variables as they are on the y-axis of the graph. Does anyone know how to do this? I put here an example picture.

Obs.: Sorry, I was not able to add a minimal reproducible example. I don't know how to paste the Jupyter Notebook cells here, so I've pasted below the link to the code shared via Github.

In this example, the list would be "vB0 , mB1 , vB1, mB2, mB0, vB2".

minimal reproducible example

I've created a script to parse few data points from an htmlfile link and write the same to a csv file according to this format.

I do locate the fields accordingly using the selectors I've already defined within the script, but I can't stratify the output in the right way so that I can write them later to a csv file.

location of data points:

I'm using a support vector machine as a classifier for financial market data

I have a database with 1500 data records

then I do the pre-processing and division and training and testing

I want to connect node inside one big circle to node inside another big circle or sometimes to another bigger circle itself. Is there a way to achieve the same ? I am able to connect nodes inside the same circle.

Below is the sample code that I have tried with :

So, this is something I think I'm complicating far too much but it also has some of my other colleagues stumped as well.

I've got a set of areas represented by polygons and I've got a column in the dataframe holding their areas. The distribution of areas is heavily right skewed. Essentially I want to randomly sample them based upon a distribution of sampling probabilities that is inversely proportional to their area. Rescaling the values to between zero and one (using the {​​​​​​​​x-min(x)}​​​​​​​​/{​​​​​​​​max(x)-min(x)}​​​​​​​​ method) and subtracting them from 1 would seem to be the intuitive approach, but this would simply mean that the smallest are almost always the one sampled.

I'd like a flatter (but not uniform!) right-skewed distribution of sampling probabilities across the values, but I am unsure on how to do this while taking the area values into account. I don't think stratifying them is what I am looking for either as that would introduce arbitrary bounds on the probability allocations.

Reproducible code below with the item of interest (the vector of probabilities) given by prob_vector. That is, how to generate prob_vector given the above scenario and desired outcomes?

My dataset is evenly split between 0 and 1 classifiers. 100,000 data points total with 50,000 being classified as 0 and another 50,000 classified as 1. I did an 80/20 split to train/test the data and returned a 98% accuracy score. However, when looking at the confusion matrix I have an awful lot of false positives. I'm new to xgboost and decision trees in general. What settings can I change in the XGBClassifier to reduce the number of false positives or is it even possible? Thank you.

I defined my X and y as follows:

I'm using training data set (i.e., X_train, y_train) when tuning the hyperparameters of my model. I need to use the test data set (i.e., X_test, y_test) as a final check, to make sure my model isn't biased. I wrote