Machine_Learning_Aplicado/proyecto-final/outputs/bitacora_experimentos.csv

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