aleleba/Machine_Learning_Aplicado
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Agregando Proyecto Final

Browse Source
This commit is contained in:
Alejandro Lembke Barrientos
2026-06-24 07:43:49 +00:00
parent 1f4893730a
commit 5ba20605c3
9 changed files with 1507 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
proyecto-final/outputs/bitacora_experimentos.csv Normal file
View File

@@ -0,0 +1,16 @@
experimento_id,modelo,hiperparametros,arquitectura,precision_train,recall_train,f1_train,accuracy_train,precision_test,recall_test,f1_test,accuracy_test
RF_01,random_forest,"{'n_estimators': 100, 'max_depth': None, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'criterion': 'gini'}",,1.0,1.0,1.0,1.0,0.9349176812697626,0.9308725039707921,0.9327724377144396,0.9203084832904884
RF_02,random_forest,"{'n_estimators': 200, 'max_depth': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'criterion': 'gini'}",,0.9731775711190289,0.9686270125964206,0.9707547781581585,0.965191036002939,0.9347906630645412,0.9294541680440085,0.9319017166110111,0.9199412412780023
RF_03,random_forest,"{'n_estimators': 200, 'max_depth': 20, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'criterion': 'gini'}",,0.9903948234299282,0.9886133978226317,0.9894877918994095,0.9880602498163116,0.9334076449913921,0.9290154634490754,0.9310578989186767,0.9188395152405435
RF_04,random_forest,"{'n_estimators': 500, 'max_depth': None, 'min_samples_leaf': 5, 'max_features': 'log2', 'criterion': 'gini'}",,0.9698886043179353,0.9669393350582488,0.9683599857843987,0.9636296840558413,0.9337158223094045,0.9298809359586047,0.931652433121954,0.9192067572530297
RF_05,random_forest,"{'n_estimators': 300, 'max_depth': None, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'criterion': 'entropy'}",,1.0,1.0,1.0,1.0,0.9334557081244913,0.9299161263046146,0.9315722997775326,0.9199412412780023
XGB_01,xgboost,"{'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.3, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_lambda': 1}",,0.9999496627403605,0.9999322630901578,0.9999409504488485,0.9999081557678178,0.9381833017622513,0.9345106923717922,0.9362721257358918,0.9232464193903782
XGB_02,xgboost,"{'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.1, 'subsample': 0.8, 'colsample_bytree': 0.8, 'reg_lambda': 1}",,0.9995124631182911,0.9993828425668196,0.9994472778499259,0.9991734019103601,0.9400145643891352,0.9355998108933601,0.9377337700729521,0.9258171134777818
XGB_03,xgboost,"{'n_estimators': 300, 'max_depth': 4, 'learning_rate': 0.05, 'subsample': 0.8, 'colsample_bytree': 0.8, 'reg_lambda': 2}",,0.9738431057592221,0.9714270969690408,0.972586594741007,0.964731814842028,0.9390495539072736,0.935051375447481,0.9369779245757606,0.9239809034153507
XGB_04,xgboost,"{'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.1, 'subsample': 0.6, 'colsample_bytree': 0.6, 'reg_lambda': 5}",,0.9979483687785702,0.9974251802181292,0.9976852599170984,0.9971528288023512,0.9360823265498792,0.9320238330654388,0.9339747695658638,0.9206757253029747
XGB_05,xgboost,"{'n_estimators': 500, 'max_depth': 6, 'learning_rate': 0.01, 'subsample': 0.8, 'colsample_bytree': 0.8, 'reg_lambda': 1}",,0.9711283975656463,0.968488525587758,0.9697589584183979,0.960966201322557,0.9388577890936005,0.9344234986407992,0.9365260895250824,0.9243481454278369
NN_01,neural_network,"{'capas': (64,), 'activacion': 'relu', 'dropout': 0.0, 'lr': 0.001, 'batch_size': 32, 'epochs': 50}",[64]→softmax,0.9514075483337615,0.9497502280290414,0.9505399462571212,0.9393828067597355,0.9382200228163496,0.9367635261121906,0.9373738091629885,0.9265515975027543
NN_02,neural_network,"{'capas': (128, 64), 'activacion': 'relu', 'dropout': 0.3, 'lr': 0.001, 'batch_size': 64, 'epochs': 100}","[128,64]+Dropout(0.3)→softmax",0.9544504646790793,0.951693687099693,0.95297564727531,0.942505510653931,0.942351208286615,0.9395367498811359,0.9408371424891058,0.9302240176276166
NN_03,neural_network,"{'capas': (256, 128, 64), 'activacion': 'relu', 'dropout': 0.3, 'lr': 0.001, 'batch_size': 64, 'epochs': 100}","[256,128,64]+Dropout(0.3)→softmax",0.9596197331839835,0.9560282590407893,0.9576542667718927,0.9473732549595886,0.9403300598776779,0.9375579241769321,0.9387617954675829,0.9283878075651855
NN_04,neural_network,"{'capas': (128, 64), 'activacion': 'relu', 'dropout': 0.2, 'lr': 0.0001, 'batch_size': 32, 'epochs': 150}","[128,64]+Dropout(0.2)→softmax(lr=0.0001)",0.9515492560718786,0.9492242953999462,0.950294386638384,0.9401175606171932,0.9396175891521139,0.9377347900370906,0.9386047132899191,0.9280205655526992
NN_05,neural_network,"{'capas': (256, 128), 'activacion': 'relu', 'dropout': 0.4, 'lr': 0.001, 'batch_size': 128, 'epochs': 100}","[256,128]+Dropout(0.4)→softmax",0.9554800613552519,0.9537600530164673,0.9545517843251645,0.9441587068332109,0.9395108985072946,0.9372280998211393,0.9382606172667207,0.9272860815277267
1 experimento_id modelo hiperparametros arquitectura precision_train recall_train f1_train accuracy_train precision_test recall_test f1_test accuracy_test
2 RF_01 random_forest {'n_estimators': 100, 'max_depth': None, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'criterion': 'gini'} 1.0 1.0 1.0 1.0 0.9349176812697626 0.9308725039707921 0.9327724377144396 0.9203084832904884
3 RF_02 random_forest {'n_estimators': 200, 'max_depth': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'criterion': 'gini'} 0.9731775711190289 0.9686270125964206 0.9707547781581585 0.965191036002939 0.9347906630645412 0.9294541680440085 0.9319017166110111 0.9199412412780023
4 RF_03 random_forest {'n_estimators': 200, 'max_depth': 20, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'criterion': 'gini'} 0.9903948234299282 0.9886133978226317 0.9894877918994095 0.9880602498163116 0.9334076449913921 0.9290154634490754 0.9310578989186767 0.9188395152405435
5 RF_04 random_forest {'n_estimators': 500, 'max_depth': None, 'min_samples_leaf': 5, 'max_features': 'log2', 'criterion': 'gini'} 0.9698886043179353 0.9669393350582488 0.9683599857843987 0.9636296840558413 0.9337158223094045 0.9298809359586047 0.931652433121954 0.9192067572530297
6 RF_05 random_forest {'n_estimators': 300, 'max_depth': None, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'criterion': 'entropy'} 1.0 1.0 1.0 1.0 0.9334557081244913 0.9299161263046146 0.9315722997775326 0.9199412412780023
7 XGB_01 xgboost {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.3, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_lambda': 1} 0.9999496627403605 0.9999322630901578 0.9999409504488485 0.9999081557678178 0.9381833017622513 0.9345106923717922 0.9362721257358918 0.9232464193903782
8 XGB_02 xgboost {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.1, 'subsample': 0.8, 'colsample_bytree': 0.8, 'reg_lambda': 1} 0.9995124631182911 0.9993828425668196 0.9994472778499259 0.9991734019103601 0.9400145643891352 0.9355998108933601 0.9377337700729521 0.9258171134777818
9 XGB_03 xgboost {'n_estimators': 300, 'max_depth': 4, 'learning_rate': 0.05, 'subsample': 0.8, 'colsample_bytree': 0.8, 'reg_lambda': 2} 0.9738431057592221 0.9714270969690408 0.972586594741007 0.964731814842028 0.9390495539072736 0.935051375447481 0.9369779245757606 0.9239809034153507
10 XGB_04 xgboost {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.1, 'subsample': 0.6, 'colsample_bytree': 0.6, 'reg_lambda': 5} 0.9979483687785702 0.9974251802181292 0.9976852599170984 0.9971528288023512 0.9360823265498792 0.9320238330654388 0.9339747695658638 0.9206757253029747
11 XGB_05 xgboost {'n_estimators': 500, 'max_depth': 6, 'learning_rate': 0.01, 'subsample': 0.8, 'colsample_bytree': 0.8, 'reg_lambda': 1} 0.9711283975656463 0.968488525587758 0.9697589584183979 0.960966201322557 0.9388577890936005 0.9344234986407992 0.9365260895250824 0.9243481454278369
12 NN_01 neural_network {'capas': (64,), 'activacion': 'relu', 'dropout': 0.0, 'lr': 0.001, 'batch_size': 32, 'epochs': 50} [64]→softmax 0.9514075483337615 0.9497502280290414 0.9505399462571212 0.9393828067597355 0.9382200228163496 0.9367635261121906 0.9373738091629885 0.9265515975027543
13 NN_02 neural_network {'capas': (128, 64), 'activacion': 'relu', 'dropout': 0.3, 'lr': 0.001, 'batch_size': 64, 'epochs': 100} [128,64]+Dropout(0.3)→softmax 0.9544504646790793 0.951693687099693 0.95297564727531 0.942505510653931 0.942351208286615 0.9395367498811359 0.9408371424891058 0.9302240176276166
14 NN_03 neural_network {'capas': (256, 128, 64), 'activacion': 'relu', 'dropout': 0.3, 'lr': 0.001, 'batch_size': 64, 'epochs': 100} [256,128,64]+Dropout(0.3)→softmax 0.9596197331839835 0.9560282590407893 0.9576542667718927 0.9473732549595886 0.9403300598776779 0.9375579241769321 0.9387617954675829 0.9283878075651855
15 NN_04 neural_network {'capas': (128, 64), 'activacion': 'relu', 'dropout': 0.2, 'lr': 0.0001, 'batch_size': 32, 'epochs': 150} [128,64]+Dropout(0.2)→softmax(lr=0.0001) 0.9515492560718786 0.9492242953999462 0.950294386638384 0.9401175606171932 0.9396175891521139 0.9377347900370906 0.9386047132899191 0.9280205655526992
16 NN_05 neural_network {'capas': (256, 128), 'activacion': 'relu', 'dropout': 0.4, 'lr': 0.001, 'batch_size': 128, 'epochs': 100} [256,128]+Dropout(0.4)→softmax 0.9554800613552519 0.9537600530164673 0.9545517843251645 0.9441587068332109 0.9395108985072946 0.9372280998211393 0.9382606172667207 0.9272860815277267