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Week3

Model E - Train Naive Bayes Classifier
Naive Bayes Learner
Scorer
Model M - Leave-One-Out Cross Validation
X-Partitioner
Model E - Predict Test Data Using Naive Bayes
Naive Bayes Predictor
Model M - Aggregate Leave-One-Out Predictions
X-Aggregator
Model E - Evaluate Precision, Recall and Accuracy
Scorer
Model M - Evaluate Leave-One-Out Performance
Scorer
Model M - Predict One Held-Out Record
Logistic Regression Predictor
Model M - Train Logistic Regression on 299 Records
Logistic Regression Learner
Model I - Evaluate Precision, Recall and Accuracy
Scorer
Best Model A - Training ROC and AUC
ROC Curve
Model F - Train Random Forest Classifier (200 Trees)
Random Forest Learner
Best Model A - Testing ROC and AUC
ROC Curve
Model I - Train Random Forest Classifier (200 Trees)
Random Forest Learner
Best Model A - Predict Training Data
Logistic Regression Predictor
Model I - Predict Test Data Using Random Forest
Random Forest Predictor
Best Model A - Evaluate Training Performance
Scorer
Model G - Evaluate Precision, Recall and Accuracy
Scorer
Model G - Train Logistic Regression Model
Logistic Regression Learner
Model F - Predict Test Data Using Random Forest
Random Forest Predictor
Model F - Evaluate Precision, Recall and Accuracy
Scorer
Model G - Predict Test Data Using Logistic Regression
Logistic Regression Predictor
Model H - Train Naive Bayes Classifier
Naive Bayes Learner
Model J
Table Partitioner
Model J - Train Logistic Regression on Random Split
Logistic Regression Learner
Model H - Predict Test Data Using Naive Bayes
Naive Bayes Predictor
Model H - Evaluate Precision, Recall and Accuracy
Scorer
Model K - Start Random Feature Selection
Feature Selection Loop Start (1:1)
Model J - Predict Random Test Data
Logistic Regression Predictor
Model J - Evaluate Random Sampling Performance
Scorer
Load Heart Failure Clinical Records Dataset
CSV Reader
Convert Binary Attributes and Target to Categorical
Number to String
Apply Z-score Normalization to Numerical Predictors
Normalizer
Suite 1 - Stratified Split (50% Train, 50% Test)
Table Partitioner
Suite 2 - Stratified Split (70% Train, 30% Test)
Table Partitioner
Suite 3 - Stratified Split (90% Train, 10% Test)
Table Partitioner
Model A - Train Logistic Regression Model
Logistic Regression Learner
Model K - Predict Feature Selection Validation Data
Random Forest Predictor
Model K - Evaluate Feature Subset Accuracy
Scorer
Model K - Split Feature Selection Data (80% Train, 20% Test)
Table Partitioner
Model K - Train Random Forest for Feature Selection
Random Forest Learner
Model K - Retain Selected Features from Normalized Data
Reference Column Filter
Model K - Retain DEATH_EVENT Target Column
Column Filter
Model K - Select Best Feature Subset Based on Accuracy
Feature Selection Loop End
Model K - Filter Normalized Dataset Using Selected Features
Feature Selection Filter
Model K - Combine Selected Features with Target
Column Appender
Model K - Stratified Split of Selected Features (50/50)
Table Partitioner
Model K - Train Logistic Regression with Selected Features
Logistic Regression Learner
Model D - Train Logistic Regression Model
Logistic Regression Learner
Model J - Evaluate ROC Curve and AUC Using Selected Features
ROC Curve
Model K - Predict Test Data Using Selected Features
Logistic Regression Predictor
Model K - Evaluate Selected Feature Model Performance
Scorer
Model A - Predict Test Data Using Logistic Regression
Logistic Regression Predictor
Model B - Train Naive Bayes Classifier
Naive Bayes Learner
Model L - Predict Held-Out Fold
Logistic Regression Predictor
Model A - Evaluate Precision, Recall and Accuracy
Scorer
Model B - Predict Test Data Using Naive Bayes
Naive Bayes Predictor
Model L - Train Logistic Regression on 9 Folds
Logistic Regression Learner
Model D - Predict Test Data Using Logistic Regression
Logistic Regression Predictor
Model K - Evaluate ROC Curve and AUC Using Selected Features
ROC Curve
Model D - Evaluate Precision, Recall and Accuracy
Scorer
Model L - Stratified 10-Fold Cross Validation (Seed 9698)
X-Partitioner
Model C - Predict Test Data Using Random Forest
Random Forest Predictor
Model C - Evaluate Precision, Recall and Accuracy
Scorer
Model B - Evaluate Precision, Recall and Accuracy
Scorer
Model L - Aggregate Predictions from 10 Folds
X-Aggregator
Model C - Train Random Forest Classifier (200 Trees)
Random Forest Learner

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Created by: User
Created at: 2026-10-15
On NodePit since: 2026-10-17
Last update: 2026-10-21
Created with KNIME version: v5.12.0

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