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GrupoD_​TPApp_​MD2026_​P2

Modelo 4: Regresión Logística

Modelo 2: Árbol de decisión

Modelo 1: Random Forest

Se prueba la hipótesis de la variable credit_history tiene gran relevancia en los algoritmos de clasificación. Las métricas obtenidas de un árbol de decisión excluyendo dicha variable son malas.

Modelo 3: Naive Bayes

Table Partitioner
Logistic Regression Learner
Logistic Regression Predictor
Table Partitioner
Rank Correlation
ROC Curve
Scorer
Scorer
Unpivot
Heatmap
Decision Tree Predictor
Scatter Plot Matrix
Decision Tree Predictor
Scorer
Decision Tree View
Decision Tree Learner
Patrones sin missing values
Random Forest Learner
Table Partitioner
Credit_History
Number to String
Column Filter
Column Renamer
Ejecutar predicciones
Random Forest Predictor
Credit_History + Predictions
Column Filter
Leemos CSV
CSV Reader
Quitamos el Loan_ID
Column Filter
Missing Values de todomenos Credit_History
Missing Value
Missing Value (Apply)
Math Formula
Math Formula
Dataset completo
Concatenate
Math Formula
Credit_History
String to Number
Random Forest Learner
Decision Tree Learner
Histogram
Scorer
Table Partitioner
Random Forest Predictor
Decision Tree View
Naive Bayes Learner
Credit_History is missing
Row Splitter
Scorer
Naive Bayes Predictor
Statistics View
Column Expressions (legacy)
Variables dummies para Property_area
One to Many
Table Partitioner
ROC Curve
Normalizer

Nodes

Extensions

Links