- Aim: To classify Steel Plates into 6 types of faul(Pastry,Z_Scratch,K_Scatch,Stains,Dirtiness,Bumps,Other_Faults).
- Techniques used : SVC(Support Vector Classifier) using rbf kernel, AdaBoost (with DecisionTreeClassifier as shallow tree)
- Metrics: 64.7% Cohen-Kappa with SVC, 53.2% Cohen-Kappa with AdaBoost.
- Tools Used: Python
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1. Aim: To classify Steel Plates into 6 types of faul(Pastry,Z_Scratch,K_Scatch,Stains,Dirtiness,Bumps,Other_Faults). 2. Techniques used : SVC(Support Vector Classifier) using rbf kernel, AdaBoost (with DecisionTreeClassifier as shallow tree) 3. Metrics: 64.7% Cohen-Kappa with SVC, 53.2% Cohen-Kappa with AdaBoost 4. Tools Used: Python
WatchfulHitman/Steel-plate-fault-prediction
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1. Aim: To classify Steel Plates into 6 types of faul(Pastry,Z_Scratch,K_Scatch,Stains,Dirtiness,Bumps,Other_Faults). 2. Techniques used : SVC(Support Vector Classifier) using rbf kernel, AdaBoost (with DecisionTreeClassifier as shallow tree) 3. Metrics: 64.7% Cohen-Kappa with SVC, 53.2% Cohen-Kappa with AdaBoost 4. Tools Used: Python
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