query: Appropriate problems for different type of ML classifiers
positive_passages.docid: 18472
positive_passages.text: Machine learning cookbook / reference card / cheatsheet?
negative_passages.docid: 142959
negative_passages.text: Do we still need to do feature selection while using Regularization algorithms?
negative_passages.docid: 104182
negative_passages.text: regression for binary classification
negative_passages.docid: 156500
negative_passages.text: SVM for unbalanced data
negative_passages.docid: 48283
negative_passages.text: Feature selection and cross-validation
negative_passages.docid: 124976
negative_passages.text: How to do one-class text classification?
negative_passages.docid: 121138
negative_passages.text: Extreme learning machine: what's it all about?
negative_passages.docid: 145064
negative_passages.text: Techniques to detect overfitting
negative_passages.docid: 109407
negative_passages.text: ROC curve and confusion matrix in classifier performance evaluation
negative_passages.docid: 91880
negative_passages.text: Suggestions for cost-sensitive learning in a highly imbalanced setting
negative_passages.docid: 81918
negative_passages.text: Multicollinearity in Decision Tree
negative_passages.docid: 48226
negative_passages.text: How to adjust the hyperparameters of MLP classifier to get more perfect performance
negative_passages.docid: 66215
negative_passages.text: Training a decision tree against unbalanced data
negative_passages.docid: 107278
negative_passages.text: Comparing SVM and logistic regression
negative_passages.docid: 83655
negative_passages.text: Assumptions behind cross-validation
negative_passages.docid: 79498
negative_passages.text: Best methods to solve class imbalance problem and why?
negative_passages.docid: 79824
negative_passages.text: Varied Activation Functions in Neural Networks