Data-Science-Projects | Data Science and Machine Learning Real Projects | Machine Learning library

by ArmandDS HTML Version: Current License: No License

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kandi X-RAY | Data-Science-Projects Summary

Data-Science-Projects is a HTML library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Spark applications. Data-Science-Projects has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Data Science and Machine Learning Real Projects

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Data-Science-Projects has a low active ecosystem.

It has 16 star(s) with 9 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Data-Science-Projects has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Data-Science-Projects is current.

Quality

Data-Science-Projects has no bugs reported.

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Data-Science-Projects has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Data-Science-Projects does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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Data-Science-Projects releases are not available. You will need to build from source code and install.

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Data-Science-Projects Key Features

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Data-Science-Projects Examples and Code Snippets

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Community Discussions

Trending Discussions on Data-Science-Projects

Key Error Running Python Script (Using Atom)

Implementing k-means with Euclidean distance vs Manhattan distance?

QUESTION

Key Error Running Python Script (Using Atom)

Asked 2020-Jul-25 at 15:49

I have never used python before. I'm following a short guide on how to use an API with Python. I'm using Atom text editor plus the Hydrogen module to run said code.

I am getting KeyError: '203' when I run the following segment.

...

ANSWER

Answered 2020-Jul-25 at 15:49

In order to retrieve a value from a standard dictionary in python with a key, it must be a valid or else a KeyError will be raised. So your code is attempting to use the key '203' with the dictionary champ_dict, however '203' is not a valid key (hence the KeyError). To see which keys are currently present in the dict, you can call the dict.keys method on champ_dict. Example would be something like

Source https://stackoverflow.com/questions/63089884

QUESTION

Implementing k-means with Euclidean distance vs Manhattan distance?

Asked 2018-Dec-05 at 21:27

I am implementing kmeans algorithm from scratch in python and on Spark. Actually, it is my homework. The problem is to implement kmeans with predefined centroids with different initialization methods, one of them is random initialization(c1) and the other is kmeans++(c2). Also, it is required to use different distance metrics, Euclidean distance, and Manhattan distance. The formula for both of them is introduced as follows:

The second formula in each section is for the corresponding cost function which is going to be minimized. I have implemented both of them but I think there is a problem. This is the graph for the cost function per iteration of kmeans using different settings:

The first graph looks fine but the second one seems to have a problem because as far as I'm concerned, the cost of kmeans must decrease after each iteration. So, What is the problem? It's from my code or formula?

And these are my functions for computing distances and cost:

...

ANSWER

Answered 2018-Dec-05 at 21:27

K-means does not minimize distances.

It minimizes the sum of squares (which is not a metric).

If you assign points to the nearest cluster by Euclidean distance, it will still minimize the sum of squares, not Euclidean distances. In particular, the sum of euclidean distances may increase.

Minimizing Euclidean distances is the Weber problem. The mean is not optimal. You need a complex geometrical median to minimize Euclidean distances.

If you assign points with Manhattan distance, it is not clear what is being minimized... You have two competing objectives. While I would assume that it will still converge, that may be tricky to prove. because using the mean may increase the sum of Manhattan distances.

I think I posted a counterexample for k-means minimizing Euclidean distance here at SO or stats.SE some time ago. So your code and analysis may even be fine - it is the assignment that is flawed.

Source https://stackoverflow.com/questions/53633262

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