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ML toolkit
Machine Learning Toolkit v.1.0.0
Terminal
pip install -i https://test.pypi.org/simple/ mltoolkit
💫 About
This Python package is made on behalf of making the AI and ML Tasks simple and productive to have a faster deployment of projects It works by automating the repeated tasks and making it effective and more productive to you
Algorithm accuracy table
Python
from mltoolkit import Regression as regressor acc_table, best_param = regressor.svm(independent, dependent) acc_table
output:
| sno | C | linear | rbf | poly | sigmoid |
|---|---|---|---|---|---|
| 1 | C 10 | 0.022506 | -0.08521 | -0.082239 | -0.099652 |
| 2 | C 100 | 0.563729 | -0.113243 | -0.084659 | -0.132517 |
| 3 | C 500 | 0.64177 | -0.10929 | -0.064037 | -0.582106 |
| 4 | C 1000 | 0.669795 | -0.102105 | -0.032889 | -2.022042 |
| 5 | C 2000 | 0.767813 | -0.090715 | 0.02603 | -6.818809 |
| 6 | C 3000 | 0.764471 | -0.079182 | 0.083426 | -14.702022 |
| 7 | C 7000 | 0.734434 | -0.028374 | 0.291094 | -73.122034 |
How to use
Python
from mlstats import Regression as regressor
acc_table, best_param = regressor._algname_(independent, dependent)
acc_table
-
replace
_algname_withsvm, decision_tree, random_forest, knnfor Regression -
replace
_algname_withdecision_tree, random_forest, knnfor Classification -
replace
Regressionwithclassifierif its a classification problem statement
Model accuracy table
Python
from mltoolkit import Regression as regressor reg_report = regressor.fit(independent, dependent) reg_report
output:
| sno | Metrics | Random Forest | Linear Regression | Poisson Regression | Decision Tree | Support Vector Machine | KNN |
|---|---|---|---|---|---|---|---|
| 1 | MSE | 20770567.875901 | 32304679.499094 | 30757741.967819 | 45999841.979685 | 172821773.971895 | 112965815.146866 |
| 2 | MAE 4557.473848 | 5683.720568 | 5545.966279 | 6782.318334 | 13146.169555 | 10628.537771 | |
| 3 | R2 2714.117549 | 3985.71256 | 3748.157793 | 3099.796517 | 8532.534486 | 7417.95403 | |
| 4 | RMSE 0.868069 | 0.794807 | 0.804632 | 0.707817 | -0.097732 | 0.282462 | |
| 5 | R2ADJ | 0.867407 | 0.793777 | 0.803653 | 0.706352 | -0.103238 | 0.278863 |
How to use
Python
from mlstats import Regression as regressor
acc_table, best_param = regressor._algname_(independent, dependent)
acc_table
-
replace
Regressionwithclassifierif its a classification problem statement -
replace
fit_modelwithfit_saveto save the best model