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01_​Simple_​Neural_​Network_​solution

Exercise: Logistic Regression

This workflow trains, applies, and evaluates a logistic regression model to predict the rank (high/low) of a house.

Exercise: Simple Neural Network1) Train a fully connected neural network using the RProp MLP Learner node.2) Apply the model to the test set and evaluate the model (MultiLayerPerceptron Predictor node and Scorer node) 3) Optional: Build a paramter optimization loop to optimize the number of layers and the number of neurons per layer. Classification: Simple Neural Network Read AmesHousing.csv Only double columnsplus rankNode 78Node 79Node 80Node 82Node 83Node 84File Reader Preprocessing RProp MLP Learner Column Filter MultiLayerPerceptronPredictor Scorer Random ForestLearner Random ForestPredictor Column Filter Excel SheetAppender (XLS) ROC Curve (local) ROC Curve (local) Exercise: Simple Neural Network1) Train a fully connected neural network using the RProp MLP Learner node.2) Apply the model to the test set and evaluate the model (MultiLayerPerceptron Predictor node and Scorer node) 3) Optional: Build a paramter optimization loop to optimize the number of layers and the number of neurons per layer. Classification: Simple Neural Network Read AmesHousing.csv Only double columnsplus rankNode 78Node 79Node 80Node 82Node 83Node 84File Reader Preprocessing RProp MLP Learner Column Filter MultiLayerPerceptronPredictor Scorer Random ForestLearner Random ForestPredictor Column Filter Excel SheetAppender (XLS) ROC Curve (local) ROC Curve (local)

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2022-02-06
Last update: 2024-12-25
Created with KNIME version: v4.1.2
Tags: classificationlogistic regressioneducation

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