query: Categorical variables with too many levels in machine learning
positive_passages.docid: 10256
positive_passages.text: Principled way of collapsing categorical variables with many levels?
negative_passages.docid: 138537
negative_passages.text: Training N classifiers for N labels vs one classifier with N labels
negative_passages.docid: 118276
negative_passages.text: Multivariate multiple regression validation
negative_passages.docid: 9757
negative_passages.text: Logistic regression with binary dependent and independent variables
negative_passages.docid: 108970
negative_passages.text: Cross tabulation of two categorical variables: recommended techniques
negative_passages.docid: 131954
negative_passages.text: Choosing IRLS over gradient descent in logistic regression
negative_passages.docid: 89791
negative_passages.text: Correlated features produce strange weights in Logistic Regression
negative_passages.docid: 7838
negative_passages.text: Coding categorical variables for regression
negative_passages.docid: 148679
negative_passages.text: Modelling with more variables than data points
negative_passages.docid: 77815
negative_passages.text: Machine Learning with intended missing values
negative_passages.docid: 66215
negative_passages.text: Training a decision tree against unbalanced data
negative_passages.docid: 141448
negative_passages.text: Horizontal Probability Tree with Level Labels
negative_passages.docid: 134322
negative_passages.text: How to encode categorical variables for neural networks
negative_passages.docid: 81918
negative_passages.text: Multicollinearity in Decision Tree
negative_passages.docid: 20019
negative_passages.text: Sample size for categorical data
negative_passages.docid: 104142
negative_passages.text: How to choose significance level for a large data set?
negative_passages.docid: 11188
negative_passages.text: Can principal component analysis be applied to datasets containing a mix of continuous and categorical variables?