Banish
Terminal
pip install -i https://test.pypi.org/simple/ mltoolkit
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
Python
from mltoolkit import Regression as regressor acc_table, best_param = regressor.svm(independent, dependent) acc_table
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 |
Python
from mlstats import Regression as regressor acc_table, best_param = regressor._algname_(independent, dependent) acc_table
replace _algname_
with svm, decision_tree, random_forest, knn
for Regression
replace _algname_
with decision_tree, random_forest, knn
for Classification
replace Regression
with classifier
if its a classification problem statement
Python
from mltoolkit import Regression as regressor reg_report = regressor.fit(independent, dependent) reg_report
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 |
Python
from mlstats import Regression as regressor acc_table, best_param = regressor._algname_(independent, dependent) acc_table
replace Regression
with classifier
if its a classification problem statement
replace fit_model
with fit_save
to save the best model