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GrupoD_​TPApp_​MD2026_​P3

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

K-Means 3 clusters

Table Partitioner
Logistic Regression Learner
Logistic Regression Predictor
Table Partitioner
Rank Correlation
ROC Curve
Scorer
Normalizer
Scorer
Unpivot
Heatmap
Decision Tree Predictor
Scatter Plot Matrix
Scatter Plot
Decision Tree Predictor
Scorer
Decision Tree View
Color Manager
Decision Tree Learner
PCA
Patrones sin missing values
Random Forest Learner
Color Manager
k-Means
Table Partitioner
Credit_History
Number to String
Column Filter
Shape Manager
Column Renamer
Numeric Distances
Silhouette Coefficient
Ejecutar predicciones
Random Forest Predictor
Color Manager
Credit_History + Predictions
Column Filter
DBSCAN
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
Shape Manager
Dataset completo
Concatenate
Math Formula
Scatter Plot
Credit_History
String to Number
Scatter Plot
Random Forest Learner
Decision Tree Learner
Scatter Plot
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

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