kaggle_seeclickfix | machine learning model for the See Click Predict | Machine Learning library
kandi X-RAY | kaggle_seeclickfix Summary
kandi X-RAY | kaggle_seeclickfix Summary
kaggle_seeclickfix is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. kaggle_seeclickfix has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
My model for the See Click Predict Fix Kaggle competition. Read the postmortem for a more detailed explanation of how it works.
My model for the See Click Predict Fix Kaggle competition. Read the postmortem for a more detailed explanation of how it works.
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Quality
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kaggle_seeclickfix has a low active ecosystem.
It has 31 star(s) with 11 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of kaggle_seeclickfix is current.
Quality
kaggle_seeclickfix has 0 bugs and 0 code smells.
Security
kaggle_seeclickfix has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
kaggle_seeclickfix code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
kaggle_seeclickfix is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
kaggle_seeclickfix 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.
kaggle_seeclickfix saves you 62 person hours of effort in developing the same functionality from scratch.
It has 162 lines of code, 22 functions and 1 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed kaggle_seeclickfix and discovered the below as its top functions. This is intended to give you an instant insight into kaggle_seeclickfix implemented functionality, and help decide if they suit your requirements.
- Return the difference between two dates .
- Fit the model
- Normalize tag .
- Set the factor .
- Compute the difference between the given data .
Get all kandi verified functions for this library.
kaggle_seeclickfix Key Features
No Key Features are available at this moment for kaggle_seeclickfix.
kaggle_seeclickfix Examples and Code Snippets
No Code Snippets are available at this moment for kaggle_seeclickfix.
Community Discussions
Trending Discussions on kaggle_seeclickfix
QUESTION
scikit-learn GridSearchCV throws RuntimeError: Cannot clone object Apply as the constructor does not set parameter fn
Asked 2020-Jan-15 at 07:55
I have the following code:
...ANSWER
Answered 2018-Jul-02 at 17:31The constructor is supposed to store parameters as attributes and nothing else.
All the action should take place in fit and transform.
See below a minimal working example
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install kaggle_seeclickfix
You can download it from GitHub.
You can use kaggle_seeclickfix like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use kaggle_seeclickfix like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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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