All functions |
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Composable Preprocessing Operators |
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Constructor for CPO Objects |
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CPO Learner Object |
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Get the Retransformation or Inversion Function from a Resulting Object |
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CPO Composition Neutral Element |
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Apply a CPO to Data |
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Split a Pipeline into Its Constituents |
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Attach a CPO to a Learner |
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Clear Retrafo and Inverter Attributes |
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CPO Composition |
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Add 'covr' coverage to CPOs |
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Apply a Function Element-Wise |
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Transform a Regression Target Variable |
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Convert All Features to Numerics |
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Caches the Result of CPO Transformations |
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“cbind” the Result of Multiple CPOs |
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Compine Rare Factors |
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Drop Constant or Near-Constant Features |
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CPO Dummy Encoder |
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Filter Features: “anova.test” |
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Filter Features: “carscore” |
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Filter Features: “chi.squared” |
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Filter Features by Thresholding Filter Values |
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Filter Features: “gain.ratio” |
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Filter Features: “information.gain” |
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Filter Features: “kruskal.test” |
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Filter Features: “linear.correlation” |
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Filter Features: “mrmr” |
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Filter Features: “oneR” |
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Filter Features: “permutation.importance” |
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Filter Features: “rank.correlation” |
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Filter Features: “relief” |
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Filter Features: “cforest.importance” |
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Filter Features: “randomForest.importance” |
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Filter Features: “randomForestSRC.rfsrc” |
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Filter Features: “randomForestSRC.var.select” |
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Filter Features: “symmetrical.uncertainty” |
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Filter Features: “univariate.model.score” |
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Filter Features: “variance” |
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Clean Up Factorial Features |
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Construct a CPO for ICA Preprocessing |
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Impact Encoding |
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Impact Encoding |
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Impute and Re-Impute Data |
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Perform Imputation with Constant Value |
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Perform Imputation with Random Values |
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Perform Imputation with an |
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Perform Imputation with Multiple of Minimum |
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Perform Imputation with Mean Value |
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Perform Imputation with Median Value |
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Perform Imputation with Multiple of Minimum |
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Perform Imputation with Mode Value |
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Perform Imputation with Normally Distributed Random Values |
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Perform Imputation with Uniformly Random Values |
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Log-Transform a Regression Target Variable. |
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Create Columns from Expressions |
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Convert Data into Factors Indicating Missing Data |
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Create a “Model Matrix” from the Data Given a Formula |
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Over- or Undersample Binary Classification Tasks |
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Construct a CPO for PCA Preprocessing |
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Probability Encoding |
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Split Numeric Features into Quantile Bins |
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Train a Model on a Task and Return the Residual Task |
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Use the “se” |
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Sample Data from a Task |
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Construct a CPO for Scaling / Centering |
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Max Abs Scaling CPO |
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Range Scaling CPO |
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Drop All Columns Except Certain Selected Ones from Data |
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Perform SMOTE Oversampling for Binary Classification |
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Scale Rows to Unit Length |
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Transform CPO Hyperparameters |
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CPO Wrapper |
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defined to avoid problems with the static type checker |
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defined to avoid problems with the static type checker |
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Get the Selection Arguments for Affected CPOs |
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Get the CPO Class |
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Get the CPOConstructor Used to Create a CPO Object |
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Get the ID of a CPO Object |
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Get the CPO Object's Name |
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Determine the Operating Type of the CPO |
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Get the CPO |
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Get the Properties of the Given CPO Object |
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Get CPO Used to Train a Retrafo / Inverter |
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Get the CPOTrained's Capabilities |
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Get the Internal State of a CPORetrafo Object |
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Get the Learner with the CPOs Removed |
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Get the CPO Associated with a Learner |
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CPO Composition / Attachment / Application Operator |
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Check Whether Two CPO are Fundamentally the Same |
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Invert Target Preprocessing |
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Check CPOInverter |
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Check for NULLCPO |
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Check CPORetrafo |
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List all Built-in CPOs |
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Create a Custom CPO Constructor |
Build Data-Dependent CPOs |
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CPO Multiplexer |
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Create a CPOTrained with Given Internal State |
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Composable Preprocessing Operators |
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NULL to NULLCPO |
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NULLCPO to NULL |
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Turn the argument list into a |
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Turn a |
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Print CPO Objects |
Set the ID of a CPO Object |
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defined to avoid problems with the static type checker |