machine learning – selection of the decision tree according to error rate (what does this mean?)

I'm asked to choose an attribute based on its error rate

Could someone please explain this to me?

For example, suppose I have this data for reasons

For an attribute

3 Yes, that is classified as +

1 Yes, classified as –

4 No, classified as –

2 No, classified as +

What is the error rate?

machine learning – sckit regression of the performance data set

How do I do a linear regression for each subset of the data frame in a loop with Scikit-Learn's linear regression?

    def sub_lists(list1):
  sublist = (()) 
  for i in range(len(list1) + 1): 
        for j in range(i + 1, len(list1) + 1): 
            sub = list1(i:j) 
            sublist.append(sub) 
            return sublist

X = sub_lists(df5);y = df4;

I did a regression but it keeps getting an error, it is a .dta (stata) file.

machine learning – how does the BERT model (in tensor flow or paddle-paddle frameworks) relate to nodes of the underlying neural network that is being trained?

The BERT model in frameworks such as TensorFlow / Paddle-Paddle shows different types of calculation nodes (such as subtracting, accumulating, adding, multiplying, etc.) in a graphical form in 12 levels.

However, this diagram does not look like a neural network that is typically shown in textbooks (e.g. https://en.wikipedia.org/wiki/Artificial_neural_network#/media/File:Colored_neural_network.svg), in which each Edge is a weight that is being trained and there is an input and an output layer.

If I print out the BERT diagram instead, I cannot find out how a node in the BERT diagram relates to a node in the trained neural network.

I used the BERT framework models to compile them into a form in which we can run the model on a PC / CPU. But I am still missing this fundamental aspect of how BERT relates to the neural network, since I don't see which neural network topology is being trained (how I would expect topology / connections between / between different layers / nodes of the neural network to determine how that Training of the neural network takes place).

Could someone explain which underlying neural network is being trained by BERT? How are nodes in the BERT diagram related to neural network nodes and weights on neural network edges?

Machine learning – clustering customers with string data

I am looking for a customer clustering solution. I've done a lot of research at the machine learning level to find algorithms that might meet my needs, but I can't find a lot of information if the data is mostly text.

I was looking for string clustering but found (for me) little relevance. Do you have ideas for algorithms that could be used?

Type of relevant data:

machine learning – The purpose of tracking and predicting the position of the car while driving

I wonder the purpose of Tracking and prediction for a self-driving car? I can know place to the the next time through a lot of tracking and prediction calculation. I wonder what the place will be used for next time. Thank you for your reply.

Asymptotic analysis for machine learning algorithms

I wanted to know if it would be practical and useful to analyze machine learning algorithms for asymptotic computational complexity.

I noticed that this is very unusual. However, I believe that it would help us to compare these algorithms and decide which one to use for a particular scenario.

I am also aware that the runtime of most machine learning algorithms depends heavily on the data. For example, the gradient descent algorithm may iterate much more frequently in certain data sets than in others.

Given that, what would be a good measure of the complexity of comparing machine learning algorithms?

Run the application that requires Opengl 3.3 in the Virtualbox Virtual Machine (Windows Host and Windows Guest).

I have a program that I want to run in a virtual machine (not on my host), but it completely rejects it without running opengl 3.3. I want to be able to run this program without it crashing immediately (even if the performance isn't great). Is there a way to get opengl 3.3 up and running in Virtualbox? If not, is there a way to emulate opengl 3.3 in the virtual machine so that the program thinks it's installed? Or is there another way to get this program up and running? If so, how?

My host is Windows 10 and the guest is Windows 10. I am using Virtualbox 5.2.16. This is the error I get when I try to run the program on a VM: Enter image description here

I am just looking for a way out and am open to any suggestions.

restore – Migration from MacBook Catalina to MacBook High Sierra with Time Machine OS + Data

Every time I try to clone my current MacBook from a Time Machine backup to a newer MacBook with High Sierra, it fails. The efilogin helper is terminated due to "No further connections". Then I get the message "The library of the Quartz framework could not be loaded". Tried three times without success.

I tried to create a somewhat older Time Machine backup file, but I have little hope that this will make a difference.

Any suggestions?