vaststatus.blogg.se - Caret logistic regression

choose the “optimal” model across these parameters.

evaluate, using resampling, the effect of model tuning parameters on performance.

The caret package has several functions that attempt to streamline the model building and evaluation process.

22.2 Internal and External Performance Estimates.

22 Feature Selection using Simulated Annealing.

21.2 Internal and External Performance Estimates.

21 Feature Selection using Genetic Algorithms.

20.3 Recursive Feature Elimination via caret.

20.2 Resampling and External Validation.

19 Feature Selection using Univariate Filters.

18.1 Models with Built-In Feature Selection.

16.6 Neural Networks with a Principal Component Step.

16.2 Partial Least Squares Discriminant Analysis.

16.1 Yet Another k-Nearest Neighbor Function.

13.9 Illustrative Example 6: Offsets in Generalized Linear Models.13.8 Illustrative Example 5: Optimizing probability thresholds for class imbalances.13.7 Illustrative Example 4: PLS Feature Extraction Pre-Processing.13.6 Illustrative Example 3: Nonstandard Formulas.13.5 Illustrative Example 2: Something More Complicated - LogitBoost.13.2 Illustrative Example 1: SVMs with Laplacian Kernels.12.1.2 Using additional data to measure performance.12.1.1 More versatile tools for preprocessing data.11.4 Using Custom Subsampling Techniques.7.0.27 Multivariate Adaptive Regression Splines.5.9 Fitting Models Without Parameter Tuning.5.8 Exploring and Comparing Resampling Distributions.5.7 Extracting Predictions and Class Probabilities.5.1 Model Training and Parameter Tuning.4.4 Simple Splitting with Important Groups.4.1 Simple Splitting Based on the Outcome.3.2 Zero- and Near Zero-Variance Predictors.Loaded via a namespace (and not attached): parallel splines stats graphics grDevices utils datasets methods base Rdata file with the df_without data frame is here. In lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, :ĭo I need to convert my dataframe to a matrix or not?ĭoes my response variable need to be a factor or just 0/1 integers? NA/NaN/Inf in foreign function call (arg 5) I've tried two different syntaxes, but they both throw an error: fitControl <- trainControl(method = "repeatedcv",Įrror in cut.default(y, breaks, include.lowest = TRUE) :ĭf_without$response <- as.factor(df_without$response)įit_logistic <- train(as.matrix(df_without), df_without$response,Įrror in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : I'm trying to fit a logistic regression model to my data, using glmnet (for lasso) and caret (for k-fold cross-validation).