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JKISeason3-18_​tofusa

Description: You work as a researcher creating models to identify whether a breast tumor is benign or malign, based on anonymized patient data. Besides obtaining a classifier that works very well for both benign and malign cases, you must be able to explain how different feature values impact your results. Experiment with LIME and visualization techniques to explain your predictions and make your research more transparent. Hint: Learn more about this problem's data attributes here.

URL: LIME https://www.knime.com/blog/XAI-LIME-stroke-prediction
URL: Breast Cancer Wisconsin (Original) https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+original

Level: HardDescription: You work as a researcher creating models to identify whether a breast tumor is benign or malign, based on anonymized patientdata. Besides obtaining a classifier that works very well for both benign and malign cases, you must be able to explain how different featurevalues impact your results. Experiment with LIME and visualization techniques to explain your predictions and make your research moretransparent. Hint: Learn more about this problem's data attributes here.説明 あなたは研究者として、匿名化された患者データに基づいて、乳腺腫瘍が良性か悪性かを識別するモデルを作成します。良性と悪性の両方のケースで非常にうまく機能する分類器を得るだけでなく、異なる特徴値が結果にどのような影響を与えるかを説明できなければなりません。予測を説明し、研究をより透明化するために、LIMEや可視化テクニックを試してみましょう。ヒント：この問題のデータ属性については、こちらをご覧ください。 decide best modelload csv data top input : test set instance rows to be explainedbottom input : test set distributioncollectsexplanationsPredictNode 1691FeaturecontributionNode 2018Node 2020Put multiple elementsinto one cellNode 2035Node 2039Conacatenatethe influential featuresNode 2046set clasification targettraining a local GLMfor each input instance to generate a Local Inter. Model-agn. ExplanationNode 2138Node 2139AutoML CSV Reader LIME Loop Start Loop End Table Transposer Column Filter RowID Workflow Executor Column Renamer Bar Chart Partitioning Image to Table Column Appender GroupBy Group Loop Start Loop End Metanode Column Appender Rule Engine Compute LIME Equal Size Sampling Normalizer view Level: HardDescription: You work as a researcher creating models to identify whether a breast tumor is benign or malign, based on anonymized patientdata. Besides obtaining a classifier that works very well for both benign and malign cases, you must be able to explain how different featurevalues impact your results. Experiment with LIME and visualization techniques to explain your predictions and make your research moretransparent. Hint: Learn more about this problem's data attributes here.説明 あなたは研究者として、匿名化された患者データに基づいて、乳腺腫瘍が良性か悪性かを識別するモデルを作成します。良性と悪性の両方のケースで非常にうまく機能する分類器を得るだけでなく、異なる特徴値が結果にどのような影響を与えるかを説明できなければなりません。予測を説明し、研究をより透明化するために、LIMEや可視化テクニックを試してみましょう。ヒント：この問題のデータ属性については、こちらをご覧ください。 decide best modelload csv data top input : test set instance rows to be explainedbottom input : test set distributioncollectsexplanationsPredictNode 1691FeaturecontributionNode 2018Node 2020Put multiple elementsinto one cellNode 2035Node 2039Conacatenatethe influential featuresNode 2046set clasification targettraining a local GLMfor each input instance to generate a Local Inter. Model-agn. ExplanationNode 2138Node 2139AutoML CSV Reader LIME Loop Start Loop End Table Transposer Column Filter RowID Workflow Executor Column Renamer Bar Chart Partitioning Image to Table Column Appender GroupBy Group Loop Start Loop End Metanode Column Appender Rule Engine Compute LIME Equal Size Sampling Normalizer view

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Created by: user1
Created at: 2024-05-23
On NodePit since: 2024-09-14
Last update: 2024-10-04
Created with KNIME version: v5.3.1
Tags: JKISeason3-18

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