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This python package is mde to simplify your ml tasks and make you to run large program at few lines of code it simplifies your program and imprpve your productivity.
from mltoolkit import Regression as regressor
acc_table, best_param = regressor.svm(independent, dependent)
acc_table
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 |
from mltoolkit import Regression as regressor
acc_table, best_param = regressor._algname_(independent, dependent)
acc_table
-
replace
_algname_
withsvm, decision_tree, random_forest, knn
for Regression -
replace
_algname_
withdecision_tree, random_forest, knn
for Classification -
replace
Regression
withclassifier
if its a classification problem statement
from mltoolkit import Regression as regressor
reg_report = regressor.fit_model(independent, dependent)
reg_report
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 |
from mltoolkit import Regression as regressor
reg_report = regressor.fit_model(independent, dependent)
reg_report
-
replace
Regression
withclassifier
if its a classification problem statement -
replace
fit_model
withfit_save
to save the best model