Breast-Cancer-Wisconsin-Diagnostic | Prediction of Benign or Malignant Cancer Tumors | Machine Learning library

by officialpm Jupyter Notebook Version: v1.0 License: MIT

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kandi X-RAY | Breast-Cancer-Wisconsin-Diagnostic Summary

Breast-Cancer-Wisconsin-Diagnostic is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning applications. Breast-Cancer-Wisconsin-Diagnostic has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

2-31) Ten synthetic-valued features are computed for each cell nucleus:. The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.

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Breast-Cancer-Wisconsin-Diagnostic has a low active ecosystem.

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

It had no major release in the last 12 months.

Breast-Cancer-Wisconsin-Diagnostic has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Breast-Cancer-Wisconsin-Diagnostic is v1.0

Quality

Breast-Cancer-Wisconsin-Diagnostic has 0 bugs and 0 code smells.

Security

Breast-Cancer-Wisconsin-Diagnostic has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Breast-Cancer-Wisconsin-Diagnostic code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Breast-Cancer-Wisconsin-Diagnostic is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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Breast-Cancer-Wisconsin-Diagnostic releases are available to install and integrate.

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Breast-Cancer-Wisconsin-Diagnostic Key Features

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Community Discussions

Trending Discussions on Breast-Cancer-Wisconsin-Diagnostic

Low accuracy binary classification with Pytorch

QUESTION

Low accuracy binary classification with Pytorch

Asked 2020-Oct-17 at 07:56

In practicing deep learning for binary classification with Pytorch on Breast-Cancer-Wisconsin-Diagnostic-DataSet.

I've tried different approaches, and the best I can get as below, the accuracy is still low at 61%.

What's the way to improve the accuracy?

Thank you.

...

ANSWER

Answered 2020-Oct-17 at 07:56

Features Representing samples are in different range. So, First thing you should do is to normalize the data.

You should plot the loss and acc over the training epochs for training and validation/test dataset to understand whether the model overfits on training data or underfit.

Furthermore, you can try with more complex (deeper) model. And since your training dataset has few number of samples, you can consider augmentation and transfer learning as well if possible.

Source https://stackoverflow.com/questions/64398085

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

Vulnerabilities

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Install Breast-Cancer-Wisconsin-Diagnostic

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