when set to True, reverses the sort order that is imposed by the order parameter.

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

specifies the reference level to use when you specify a nonsingular parameterization in the param parameter. For an individual variable, you can specify the level of the variable to use as the reference level. If the action supports the global class options parameter, then you can specify FIRST or LAST.

labels the difference in predictive margins in output tables.

names the difference in predictive margins in output tables.

specifies the variables to modify in a predictive margin and the values they are set to.

The bartScoreMargin_varValue value can be one or more of the following:

labels the predictive margin in output tables.

during the training phase, treats missing values for continuous predictors as the largest machine value and treats missing values for categorical predictors as a separate level. In the scoring phase, observations that have missing continuous predictor values are assigned to the right branch of the split, and observations that have an unknown categorical predictor level are assigned to the larger branch of the split.

during the training phase, treats missing values for continuous predictors as the smallest machine value and treats missing values for categorical predictors as a separate level. In the scoring phase, observations that have missing continuous predictor values are assigned to the left branch of the split, and observations that have an unknown categorical predictor level are assigned to the larger branch of the split.

during the training phase, excludes all observations that have a missing predictor value. In the scoring phase, observations that have missing values or observations whose unknown categorical predictor level is unknown are assigned to the larger branch of the split.

during the training phase, treats missing values for continuous predictors as a separate group and treats missing values for categorical predictors as a separate level. In the training phase, when a split operation is sampled for a continuous predictor and there are observations that have a missing value of the splitting variable on the node, a primary rule for routing missing values is sampled before the primary splitting rule for nonmissing values is sampled. If a continuous predictor does not have a missing value on the node that you are splitting, a primary rule for routing missing values is not sampled. In the scoring phase, observations that have an unknown categorical predictor level or have a missing continuous predictor value for a node without a primary rule for routing missing values are assigned to the larger branch of the split.

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

specifies a list of effects that define the model. Each term in this list is made up of variables specified in the vars parameter and their interaction (which can be NONE, CROSS, or BAR). When the interaction is BAR, it can be limited by the maxInteract parameter.

specifies the significance level to use for the construction of all equal-tail credible limits.

when set to FALSE, predictions from each MCMC sample are included in the output table in addition to the sample average predictions.

specifies a list of one or more variables to be copied from the input table to the output table. You can alternatively specify the value ALL, ALL_MODEL, or ALL_NUMERIC, which respectively copies all variables, all variables used in the modeling, or all numeric variables from the input table to the output table.

names the predicted response level. The default name is Into.

specifies the predicted event probability that determines the predicted binary response level.

names the equal-tail lower credible limit.

names the predicted value. If you do not specify any output statistics, then the predicted value is named Pred by default.

names the residual.

identifies the training and test roles for observations.

names the equal-tail upper credible limit.

specifies the seed to use in the random number generator that is used for partitioning the data.

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

specifies the formatted value of the variable that is used to assign observations to the testing role.

specifies the formatted value of the variable that is used to assign observations to the training role. If you do not specify the train parameter, then all observations whose roles are not determined by the test and validate parameters are assigned to training.

names the variable that contains average number of nodes per tree in the sample.

names the variable that contains proportion of accepted tree modifications.

names the variable that contains an indicator for whether the sample is saved for prediction.

specifies the base probability for splitting an internal node as a function of its depth from the root. A larger base probability value makes splitting a node more likely.

specifies the power parameter used to compute the probability of splitting an internal node as a function of its depth from the root. A larger depth power value decreases the probability of splitting a node.

specifies the probability of sampling the operation of pruning a pair of terminal nodes for the tree sampling algorithm. If you specify the pSplit and pPrune parameters, their values must sum to 1.

specifies the probability of sampling the operation of splitting a terminal node for the tree sampling algorithm. If you specify the pSplit and pPrune parameters, their values must sum to 1.

when set to True, reverses the sort order that is imposed by the order parameter.

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

specifies the reference level to use when you specify a nonsingular parameterization in the param parameter. For an individual variable, you can specify the level of the variable to use as the reference level. If the action supports the global class options parameter, then you can specify FIRST or LAST.

labels the difference in predictive margins in output tables.

names the difference in predictive margins in output tables.

specifies the variables to modify in a predictive margin and the values they are set to.

The bartScoreMargin_varValue value can be one or more of the following:

labels the predictive margin in output tables.

during the training phase, treats missing values for continuous predictors as the largest machine value and treats missing values for categorical predictors as a separate level. In the scoring phase, observations that have missing continuous predictor values are assigned to the right branch of the split, and observations that have an unknown categorical predictor level are assigned to the larger branch of the split.

during the training phase, treats missing values for continuous predictors as the smallest machine value and treats missing values for categorical predictors as a separate level. In the scoring phase, observations that have missing continuous predictor values are assigned to the left branch of the split, and observations that have an unknown categorical predictor level are assigned to the larger branch of the split.

during the training phase, excludes all observations that have a missing predictor value. In the scoring phase, observations that have missing values or observations whose unknown categorical predictor level is unknown are assigned to the larger branch of the split.

during the training phase, treats missing values for continuous predictors as a separate group and treats missing values for categorical predictors as a separate level. In the training phase, when a split operation is sampled for a continuous predictor and there are observations that have a missing value of the splitting variable on the node, a primary rule for routing missing values is sampled before the primary splitting rule for nonmissing values is sampled. If a continuous predictor does not have a missing value on the node that you are splitting, a primary rule for routing missing values is not sampled. In the scoring phase, observations that have an unknown categorical predictor level or have a missing continuous predictor value for a node without a primary rule for routing missing values are assigned to the larger branch of the split.

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

specifies a list of effects that define the model. Each term in this list is made up of variables specified in the vars parameter and their interaction (which can be NONE, CROSS, or BAR). When the interaction is BAR, it can be limited by the maxInteract parameter.

specifies the significance level to use for the construction of all equal-tail credible limits.

when set to FALSE, predictions from each MCMC sample are included in the output table in addition to the sample average predictions.

specifies a list of one or more variables to be copied from the input table to the output table. You can alternatively specify the value ALL, ALL_MODEL, or ALL_NUMERIC, which respectively copies all variables, all variables used in the modeling, or all numeric variables from the input table to the output table.

names the predicted response level. The default name is Into.

specifies the predicted event probability that determines the predicted binary response level.

names the equal-tail lower credible limit.

names the predicted value. If you do not specify any output statistics, then the predicted value is named Pred by default.

names the residual.

identifies the training and test roles for observations.

names the equal-tail upper credible limit.

specifies the seed to use in the random number generator that is used for partitioning the data.

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

specifies the formatted value of the variable that is used to assign observations to the testing role.

specifies the formatted value of the variable that is used to assign observations to the training role. If you do not specify the train parameter, then all observations whose roles are not determined by the test and validate parameters are assigned to training.

names the variable that contains average number of nodes per tree in the sample.

names the variable that contains proportion of accepted tree modifications.

names the variable that contains an indicator for whether the sample is saved for prediction.

specifies the base probability for splitting an internal node as a function of its depth from the root. A larger base probability value makes splitting a node more likely.

specifies the power parameter used to compute the probability of splitting an internal node as a function of its depth from the root. A larger depth power value decreases the probability of splitting a node.

specifies the probability of sampling the operation of pruning a pair of terminal nodes for the tree sampling algorithm. If you specify the pSplit and pPrune parameters, their values must sum to 1.

specifies the probability of sampling the operation of splitting a terminal node for the tree sampling algorithm. If you specify the pSplit and pPrune parameters, their values must sum to 1.

when set to True, reverses the sort order that is imposed by the order parameter.

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

specifies the reference level to use when you specify a nonsingular parameterization in the param parameter. For an individual variable, you can specify the level of the variable to use as the reference level. If the action supports the global class options parameter, then you can specify FIRST or LAST.

labels the difference in predictive margins in output tables.

names the difference in predictive margins in output tables.

specifies the variables to modify in a predictive margin and the values they are set to.

The bartScoreMargin_varValue value can be one or more of the following:

labels the predictive margin in output tables.

during the training phase, treats missing values for continuous predictors as the largest machine value and treats missing values for categorical predictors as a separate level. In the scoring phase, observations that have missing continuous predictor values are assigned to the right branch of the split, and observations that have an unknown categorical predictor level are assigned to the larger branch of the split.

during the training phase, treats missing values for continuous predictors as the smallest machine value and treats missing values for categorical predictors as a separate level. In the scoring phase, observations that have missing continuous predictor values are assigned to the left branch of the split, and observations that have an unknown categorical predictor level are assigned to the larger branch of the split.

during the training phase, excludes all observations that have a missing predictor value. In the scoring phase, observations that have missing values or observations whose unknown categorical predictor level is unknown are assigned to the larger branch of the split.

during the training phase, treats missing values for continuous predictors as a separate group and treats missing values for categorical predictors as a separate level. In the training phase, when a split operation is sampled for a continuous predictor and there are observations that have a missing value of the splitting variable on the node, a primary rule for routing missing values is sampled before the primary splitting rule for nonmissing values is sampled. If a continuous predictor does not have a missing value on the node that you are splitting, a primary rule for routing missing values is not sampled. In the scoring phase, observations that have an unknown categorical predictor level or have a missing continuous predictor value for a node without a primary rule for routing missing values are assigned to the larger branch of the split.

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

specifies a list of effects that define the model. Each term in this list is made up of variables specified in the vars parameter and their interaction (which can be NONE, CROSS, or BAR). When the interaction is BAR, it can be limited by the maxInteract parameter.

specifies the significance level to use for the construction of all equal-tail credible limits.

when set to FALSE, predictions from each MCMC sample are included in the output table in addition to the sample average predictions.

specifies a list of one or more variables to be copied from the input table to the output table. You can alternatively specify the value ALL, ALL_MODEL, or ALL_NUMERIC, which respectively copies all variables, all variables used in the modeling, or all numeric variables from the input table to the output table.

names the predicted response level. The default name is Into.

specifies the predicted event probability that determines the predicted binary response level.

names the equal-tail lower credible limit.

names the predicted value. If you do not specify any output statistics, then the predicted value is named Pred by default.

names the residual.

identifies the training and test roles for observations.

names the equal-tail upper credible limit.

specifies the seed to use in the random number generator that is used for partitioning the data.

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

specifies the formatted value of the variable that is used to assign observations to the testing role.

specifies the formatted value of the variable that is used to assign observations to the training role. If you do not specify the train parameter, then all observations whose roles are not determined by the test and validate parameters are assigned to training.

names the variable that contains average number of nodes per tree in the sample.

names the variable that contains proportion of accepted tree modifications.

names the variable that contains an indicator for whether the sample is saved for prediction.

specifies the base probability for splitting an internal node as a function of its depth from the root. A larger base probability value makes splitting a node more likely.

specifies the power parameter used to compute the probability of splitting an internal node as a function of its depth from the root. A larger depth power value decreases the probability of splitting a node.

specifies the probability of sampling the operation of pruning a pair of terminal nodes for the tree sampling algorithm. If you specify the pSplit and pPrune parameters, their values must sum to 1.

specifies the probability of sampling the operation of splitting a terminal node for the tree sampling algorithm. If you specify the pSplit and pPrune parameters, their values must sum to 1.