A quick way to see the best supervised learning method for your dataset or best configuration for the chosen method.
Performance Overview of Supervised Learning methods
Do not know which supervised learning method is good for your dataset? Would you like to know it in just few seconds?
|You are about to learn about a package which gives you the solution to all above problems!|
This small package of merely few bytes and code written in less than 100 lines, provide you the overview of all fundamental metrics measured for almost all supervised learning method.
|Models evaluated:||Metrics considered:|
|Decision Trees||Logistic Regression||Accuracy||Precision|
|Naive Bayes||SVM||Jaccard Score||F1_Score|
|Neural Networks||K-NN||R (Corr. Coeff.)||ROC AUC|
|Random Forest||Adaboost||MSE||Log Loss|
|Mandatory inputs required:|
A Pandas DataFrame
|Optional inputs in the given order:|
|How to install:||
Type pip install basicanalysis in command line to install the package
To call the module from this package, type from basicanalysis.basicanalysis import basicanalysis
- Note : To import other modules, use the following command.
- from basicanalysis.basicanalysis import knn from basicanalysis.basicanalysis import knn_10fold from basicanalysis.basicanalysis import *
Neat! Isn’t it?
MAJOR UPDATE: 0.0.3 -> 0.1.0, BasicAnalysis -> basicanalysis
- Added class knn to run K-NN method to compare with multiple inputs
- Added class knn_10fold to run K-nn method on training data with 10-fold cross validation, comparing multiple inputs.
README file for the task
Written in reStructuredText or .rst file, and used to generate the project page on PyPI. Images coming soon…
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|Filename, size basicanalysis-0.1.1.tar.gz (5.1 kB)||File type Source||Python version None||Upload date||Hashes View|