stroke-prediction | Stroke growth prediction | Machine Learning library

by multimodallearning Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | stroke-prediction Summary

stroke-prediction is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Numpy applications. stroke-prediction has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Stroke growth prediction

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stroke-prediction has a low active ecosystem.

It has 9 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of stroke-prediction is current.

Quality

stroke-prediction has 0 bugs and 0 code smells.

Security

stroke-prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

stroke-prediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

stroke-prediction 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.

Reuse

stroke-prediction releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

It has 2138 lines of code, 173 functions and 31 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed stroke-prediction and discovered the below as its top functions. This is intended to give you an instant insight into stroke-prediction implemented functionality, and help decide if they suit your requirements.

Train the model
Generate the file path
Save the training data to disk
Run training
Perform the forward transformation
Crop a tensor
Visualize the given epoch
Performs inference step
Visualize an epoch
Performs a single inference step
Return data loader for stroke prediction
Save the model
Argument for shape training
Argparse for step training
Loads the model
Load the model
Load training data
Calculate time to treatment
Arguments for shape prediction training
Load stroke shape data
Perform binary measures of the result
Get the time to treatment
Get arguments for shape testing
Perform the forward computation
Forward computation
Forward the convolutional layer

Get all kandi verified functions for this library.

stroke-prediction Key Features

No Key Features are available at this moment for stroke-prediction.

stroke-prediction Examples and Code Snippets

No Code Snippets are available at this moment for stroke-prediction.

Community Discussions

Trending Discussions on stroke-prediction

Confused on how to use label encoded values with original values

QUESTION

Confused on how to use label encoded values with original values

Asked 2021-May-22 at 13:35

Hi i am trying to work on a ML project where the data set contains both numeric and alphabetic values. I used LabelEncoder() from sklearn to convert alphabetic values to numeric successfully but i am unable to add all required values in the "X" "y" variables. here is my code

...

ANSWER

Answered 2021-May-22 at 13:35

Use Pandas apply with a function (transform in the example below) with the same code you already have, but using the list of columns that you want to transform over the original dataframe (data). Next, drop the target column from the dataframe (stroke in this particular dataset) to create the X variable. You also have to fill the bmi NaN values with something relevant to your analysis, otherwise the fit function will raise a ValueError.

Source https://stackoverflow.com/questions/67647560

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install stroke-prediction

Set up a Python 3.5 environment including the packages of requirements.txt file.
The experiments in the article "Learning to predict ischemic stroke growth on acute CT perfusion data by interpolating low-dimensional shape representations" have been conducted with the following parameters (command for fold 5):.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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