texts: Is the direction of edges in a Bayes Network irrelevant?

texts:
Today, in a lecture it was claimed that the direction of edges in a Bayes network doesn't really matter. They don't have to represent causality.

It is obvious that you cannot switch any single edge in a Bayes network. For example, let $G = (V, E)$ with $V = \{v_1, v_2, v_3\}$ and $E=\{(v_1, v_2), (v_1, v_3), (v_2, v_3)\}$. If you would switch $(v_1, v_3)$ to $(v_3, v_1)$, then $G$ would no longer be acyclical and hence not a Bayes network. This seems to be mainly a practical problem how to estimate the probabilities then. This case seems to be much more difficult to answer, so I will skip it.

This made me ask the following questions for which I hope to get answers here:

Is it possible for any directed acyclical graph (DAG) to reverse all edges and still have a DAG?
Assume a DAG $G$ and data is given. Now we construct the inverse DAG $G_\text{inv}$. For both DAGs, we fit the data to the corresponding Bayes networks. Now we have a set of data for which we want to use the Bayes network to predict the missing attributes. Could there be different results for both DAGs? (Bonus if you come up with an example)
Similar to 2, but simpler: Assume a DAG $G$ and data is given. You may create a new graph $G'$ by inverting any set of edges, as long as $G'$ remains acyclical. Are the Bayes networks equivalent when it comes to their predictions?
Do we get something if we have edges which do represent causality?


texts: What regression approach does tensorflow's DNNRegressor apply under the hood?

texts: I want to understand what kind of regression does the DNNRegressor estimator apply in the background, specifically is it linear?


texts: Is it valid to compare SHAP values across models?

texts:
Let's say I have three models:

a random forest with 100 trees
a random forest with 1000 trees
an xgboost model.

I can rank the importance of my features on my dataset for each model using SHAP, and compare relative importance across models. What is not clear is whether I can meaningfully compare the actual numerical SHAP values across models.
I am using the same features for all models.


texts: What does the output of model.predict function from Keras mean?

texts:
I have built a LSTM model to predict duplicate questions on the Quora official dataset. The test labels are 0 or 1. 1 indicates the question pair is duplicate. After building the model using model.fit, I test the model using model.predict on the test data. The output is an array of values something like below:

 [ 0.00514298]
 [ 0.15161049]
 [ 0.27588326]
 [ 0.00236167]
 [ 1.80067325]
 [ 0.01048524]
 [ 1.43425131]
 [ 1.99202418]
 [ 0.54853892]
 [ 0.02514757]

I am only showing the first 10 values in the array. I don't understand what do these values mean and what is the predicted label for each question pair?


texts: How to generate and visualize results of all possible parameter values in Python?

texts:
I have two parameters: demand, ticket_qty.
demand is an integer and can have a possible range of [100 - 200]
ticket_qty is also an integer and can have a possible range of [0 - 200]
I want to visualize how values for the above parameters affect the functions below:
price = demand - tickets_qty
revenue = price * ticket_qty
In Excel I created two matrixes (price and revenue) with demand as row indexes and ticket_qty as column indexes and simply applied the formula for each cell based on the rules above. However, seeing as this is a Kaggle microchallenge, I'd like to perform this using Python so as to then be able to plot and show optimal levels of price/revenue and do all my work inside the Jupyter notebook.
Questions:

How would I go about doing this in Python? pandas? numpy?
In statistics/data science, what is this "process" of mapping out possible values called? So as to facilitate further research


texts: Predicted features combined with original ones

texts:
I am currious  how good  such  a procedure could be :
I  get some predictions  of some 10  learners  trained   on train set  and predicted on train set also .
Then I am column binding   those predictions  to the original train.
Could this  be  a valid procedure  on improving  learning process?


texts: Concept of xml files for haar cascade in object detection with opencv?

texts: I've just started ML and since I entered a project on object detection, their haar cascade is used and XML files are loaded via OpenCV for detection. So I would like to understand why XML files are used. Also can I get such files for detection of any other features?