pro-grepper-org / multi-label-classification Public

A multi label classification for identifying the most probabilistic companies a problem might be asked upon in its interview. It includes several approaches like label transformation, algorithm adaption, ensemble learning and LSTM. Base classifiers like Gaussian NB, Multinomial NB, Logistic Regression, Descision Tree, Random Forest and SVC is us…

pro-grepper.herokuapp.com/

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Pro Grepper

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Preprocessing
Training and Testing
Binary Relevance
- Classifier: GaussianNB
- Classifier: MultinomialNB
- Classifier: SVC
- Classifier: Logistic regression
- Classifier: Random Forest
- Classifier: Decision Tree
Classifier Chain
- Classifier: GaussianNB
- Classifier: MultinomialNB
- Classifier: SVC
- Classifier: Logistic Regression
- Classifier: Random Forest
- Classifier: Decision Tree
Label Powerset
- Classifier: GaussianNB
- Classifier: MultinomialNB
- Classifier: SVC
- Classifier: Logisitic Regression
- Classifier: Random Forest
- Classifier: Decision Tree
Adaptive approach: MLKNN
Ensemble Learning
LSTM

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A multi label classification for identifying the most probabilistic companies a problem might be asked upon in its interview. It includes several approaches like label transformation, algorithm adaption, ensemble learning and LSTM. Base classifiers like Gaussian NB, Multinomial NB, Logistic Regression, Descision Tree, Random Forest and SVC is us…

pro-grepper.herokuapp.com/

machine-learning text-classification lstm adaptive-learning multi-label-classification mlknn chain-classifier binary-relevance problem-transformation rakel

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