Text classification With Rapid Miner
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This document discusses text classification and provides information on various models and evaluation metrics for text classification. It lists common machine learning models for text classification such as Naive Bayes, Logistic Regression, SVM, and Random Forest. It also outlines various evaluation metrics that can be used to evaluate text classification models, including precision, recall, F1-score, AUC, and accuracy. Finally, it mentions RapidMiner as an example tool for text classification.
Text classification With Rapid Miner
- 1. Text Classification Chakrit Phain R&D Softnix Technology Co.,Ltd,
- 3. • Data Preparation • Choosing Model • Evaluation • CSV • JDBC CSV Example
- 4. • Data Preparation • Choosing Model • Evaluation • CSV • JDBC Separate by
- 5. • Data Preparation • Choosing Model • Evaluation • CSV • JDBC Separate by https://prestosql.io/download.html
- 6. • Data Preparation • Choosing Model • Evaluation • CSV • JDBC Separate by https://www.cdata.com/kb/tech/presto-jdbc-rapidminer.rst
- 7. • Data Preparation • Choosing Model • Evaluation Model For Text Classification •Multinomial Naïve Bayes (NB) •Logistic Regression (LR) •SVM (SVM) •Stochastic Gradient Descent (SGD) •k-Nearest-Neighbors (kNN) •RandomForest (RF) •Gradient Boosting (GB) •XGBoost (the famous) (XGB) •Adaboost •Catboost •LigthGBM •ExtraTreesClassifier https://towardsdatascience.com/model-selection-in-text-classification-ac13eedf6146
- 8. • Data Preparation • Choosing Model • Evaluation Evaluate Metrics • Precision • Recall • F1-Score • AUC • ROC • Cohen’s Kappa • Log Loss curve • Accuracy train test curve • False/True Positive rate curve • Balanced Accuracy • Zero-one Loss • Explained Variance https://towardsdatascience.com/model-selection-in-text-classification-ac13eedf6146
- 13. Thank you