classificator | Automatically generate , train , and validate ML pipelines
kandi X-RAY | classificator Summary
kandi X-RAY | classificator Summary
classificator is a Python library. classificator has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install classificator' or download it from GitHub, PyPI.
The classificator is a wrapper around scikit-learn’s classification suite, whose primary function is to normalize and automate the process by which common machine learning classification models are trained and validated. This is specifically in the context of text feature spaces, which require NLP techniques for preprocessing and vectorization. There is an additional focus on group cross validation, where labeled groups exist as data features, and variance is known to be smaller within groups.
The classificator is a wrapper around scikit-learn’s classification suite, whose primary function is to normalize and automate the process by which common machine learning classification models are trained and validated. This is specifically in the context of text feature spaces, which require NLP techniques for preprocessing and vectorization. There is an additional focus on group cross validation, where labeled groups exist as data features, and variance is known to be smaller within groups.
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Support
classificator has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are no watchers for this library.
It had no major release in the last 12 months.
classificator has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of classificator is 0.1.7
Quality
classificator has no bugs reported.
Security
classificator has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
classificator 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
classificator releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of classificator
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of classificator
classificator Key Features
No Key Features are available at this moment for classificator.
classificator Examples and Code Snippets
No Code Snippets are available at this moment for classificator.
Community Discussions
No Community Discussions are available at this moment for classificator.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install classificator
Source Code: https://github.com/denver1117/classificator Package Index: https://pypi.python.org/pypi/classificator.
will build out a text pre-processing pipeline using a TF-IDF Vectorizer (with the supplied parameters) for the column Industry and a OneHotEncoder for the column Sector in order to predict classes CapBucket_Label, with data located here: doc/data/tickers.tsv. It will send logging, validation metrics, and a pickled model object here: tmp. It will select features from the vectorized data using Chi-Squared feature selection, attemtping multiple alpha values in grid search. It will run separate grid search processes for a Decision Tree and for a Logistic Regression using the same pre-processing pipeline. The best model will be chosen by the best Accuracy score among parameter maps across classifier choices.
See scripts/test.py for a simple example with a stock data set and configuration
Here is a sample configuration (config.json):
Running the following:
will build out a text pre-processing pipeline using a TF-IDF Vectorizer (with the supplied parameters) for the column Industry and a OneHotEncoder for the column Sector in order to predict classes CapBucket_Label, with data located here: doc/data/tickers.tsv. It will send logging, validation metrics, and a pickled model object here: tmp. It will select features from the vectorized data using Chi-Squared feature selection, attemtping multiple alpha values in grid search. It will run separate grid search processes for a Decision Tree and for a Logistic Regression using the same pre-processing pipeline. The best model will be chosen by the best Accuracy score among parameter maps across classifier choices.
See scripts/test.py for a simple example with a stock data set and configuration
Here is a sample configuration (config.json):
Running the following:
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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