Icon

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

Modelo 1: Random Forest

Modelo 4: Regresión Logística

Table Partitioner
Logistic Regression Learner
Logistic Regression Predictor
Normalizer (Apply)
Table Partitioner
Logistic Regression Learner
Rank Correlation
ROC Curve
Normalizer
Math Formula
Scorer
Math Formula
Normalizer
Math Formula
Scorer
Logistic Regression Predictor
Unpivot
Scorer
Heatmap
Decision Tree Predictor
Histogram
Scatter Plot Matrix
Scatter Plot
Decision Tree Predictor
X-Partitioner
ROC Curve
Scorer
Decision Tree View
X-Aggregator
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
Rule Engine
Decision Tree Learner
Scatter Plot
Histogram
Scorer
Pie Chart
Pie Chart
Table Partitioner
Random Forest Predictor
Normalizer
Decision Tree View
Naive Bayes Learner
Credit_History is missing
Row Splitter
Scorer
Naive Bayes Predictor
Statistics View
Random Forest Learner
Column Expressions (legacy)
Variables dummies para Property_area
One to Many
Scorer
Random Forest Predictor
Table Partitioner
X-Partitioner
ROC Curve
ROC Curve
Normalizer (Apply)
X-Aggregator

Nodes

Extensions

Links