Trainer (Classification)

Overview

You can use this Snap to build model for a classification dataset. In the Snap's settings, you can select the target field in the dataset, algorithm, and configure parameters for the selected algorithm.


Trainer (Classification) Snap Settings

Prerequisites

  • The data from upstream Snap must be in tabular format (no nested structure).
  • This Snap automatically derives the schema (field names and types) from the first document. Therefore, the first document must not have any missing values.

Limitations and known issues

None.

Snap views

View Description Examples of upstream and downstream Snaps
Input

The Snap accepts atmost one input view.
Output

The Snap produces atmost one output views:
Error

Error handling is a generic way to handle errors without losing data or failing the Snap execution. You can handle the errors that the Snap might encounter when running the pipeline by choosing one of the following options from the When errors occur list under the Views tab. The available options are:

  • Stop Pipeline Execution Stops the current pipeline execution when an error occurs.
  • Discard Error Data and Continue Ignores the error, discards that record, and continues with the remaining records.
  • Route Error Data to Error View Routes the error data to an error view without stopping the Snap execution.

Learn more about Error handling in Pipelines.

Snap settings

Legend:
  • Expression icon (): JavaScript syntax to access SnapLogic Expressions to set field values dynamically (if enabled). If disabled, you can provide a static value. Learn more.
  • SnapGPT (): Generates SnapLogic Expressions based on natural language using SnapGPT. Learn more.
  • Suggestion icon (): Populates a list of values dynamically based on your Account configuration.
  • Upload : Uploads files. Learn more.
Learn more about the icons in the Snap settings dialog.
Field / field set Type Description
Label String

Required. Specify a unique name for the Snap. Modify this to be more appropriate, especially if more than one of the same Snaps is in the pipeline.

Default value: Trainer (Classification)

Example: Classification training
Label field String/Suggestion Required.Specify the target or class field of the dataset that the model will use during the training process. This field represents the expected output values that the model learns to predict based on the input data. During inference, the model predicts this field as its output.

Default value: N/A

Example: $class
Algorithm String Required. Enter the classification algorithm that builds the model.

Default value: K-Nearest Neighbors

Example: Naive Bayes
Options String/Expression

Specify the parameters to configure the selected algorithm. These options may include hyperparameters or specific settings that influence the algorithm's behavior.

Default value: N/A

Example: max_depth=5, criterion="gini"/>
Readable Checkbox When selected, the output model is made more interpretable, focusing on readability for end-users.

Default status: Deselected
Snap execution Dropdown list Select one of the three modes in which the Snap executes.
Available options are:
  • Validate & Execute. Performs limited execution of the Snap and generates a data preview during pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during pipeline runtime.
  • Execute only. Performs full execution of the Snap during pipeline execution without generating preview data.
  • Disabled. Disables the Snap and all Snaps that are downstream from it.

Options for Algorithms

This section lists all applicable parameters for each algorithm along with their default values.

Decision Tree
Option Name Option Data Type Default Value Description
batch_size int 100 Set the preferred batch size for batch prediction.
binary_splits boolean false Enable binary splits.
collapse_tree boolean true Set to allow collapsing the tree.
confidence_factor float 0.25 Set the confidence threshold for pruning.
do_not_check_capabilities boolean false Enable capability-check.
do_not_mask_split_point_actual_value boolean false Set whether the split point actual value is to be masked.
min_num_obj int 2 Set the minimum number of instances per leaf.
num_decimal_places int 2 Set the number of decimal places.
num_folds int 3 Set the number of folds for reduced error pruning. One fold is used as pruning set.
reduced_error_pruning boolean false Enable reduced error pruning. This is false if the unpruned parameter is set to true.
save_instance_data boolean false Set whether the instance data is to be saved. If set to true, it does not clean up after the tree has been built.
seed int 1 Set the seed for random data shuffling.
subtree_raising boolean true Enable sub-tree raising.
unpruned boolean false Enable using unpruned tree. This is false if the reduced_error_pruning parameter is set to true.
use_laplace boolean false Allow Laplace smoothing for predicted probabilities.
use_md_correction boolean true Allow MDL correction for info gain on numeric attributes.
K-Nearest Neighbors
Option Name Option Data Type Default Value Description
knn int 1 Set the number of neighbors the learner will use.
batch_size int 100 Set the preferred batch size for batch prediction.
cross_validate boolean false Set whether hold-one-out cross-validation will be used to select the best k-value.
distance_weighting enum [weight_none, weight_inverse, weight_similarity] weight_none Set the distance weighting method used.
do_not_check_capabilities boolean false Enable capability-check.
mean_squared boolean false Set whether the mean squared error is used rather than mean absolute error when doing cross-validation.
nearest_neighbours_search_algorithm enum [LinearNNSearch, BallTree, CoverTree, KDTrees, FilteredNeighbourSearch] LinearNNSearch Set the nearest neighbour search algorithm to be used for finding nearest neighbor(s).
num_decimal_places int 2 Set the number of decimal places.
window_size int 0 Set the maximum number of instances allowed in the training pool.
Logistic Regression
Option Name Option Data Type Default Value Description
knn int 1 Set the number of neighbors the learner will use.
batch_size int 100 Set the preferred batch size for batch prediction.
cross_validate boolean false Set whether hold-one-out cross-validation will be used to select the best k-value.
distance_weighting enum [weight_none, weight_inverse, weight_similarity] weight_none Set the distance weighting method used.
do_not_check_capabilities boolean false Enable capability-check.
mean_squared boolean false Set whether the mean squared error is used rather than mean absolute error when doing cross-validation.
nearest_neighbours_search_algorithm enum [LinearNNSearch, BallTree, CoverTree, KDTrees, FilteredNeighbourSearch] LinearNNSearch Set the nearest neighbour search algorithm to be used for finding nearest neighbor(s).
num_decimal_places int 2 Set the number of decimal places.
window_size int 0 Set the maximum number of instances allowed in the training pool.
Logistic Regression
Option Name Option Data Type Default Value Description
batch_size int 100 Set the preferred batch size for batch prediction.
do_not_check_capabilities boolean false Enable capability-check.
matx_its int -1 Set the maximum number of iterations.
num_decimal_places int 4 Set the number of decimal places.
ridge double 1.00E-08 Sets the ridge in the log-likelihood.
use_conjugate_gradient_descent boolean false Sets whether conjugate gradient descent is used.
Naive Bayes
Option Name Data Type Default Value Description
batch_size int 100 Set the preferred batch size for batch prediction.
do_not_check_capabilities boolean false Enable capability-check.
num_decimal_places int 2 Set the number of decimal places.
use_kernel_estimator boolean false Use kernel density estimator rather than normal distribution for numeric attributes. This is false if use_supervised_discretization is set to true.
use_supervised_discretization boolean false Use supervised discretization to process numeric attributes. This is false if use_kernel_estimator is set to true.
Support Vector Machines
Option Name Data Type Default Value Description
batch_size int 100 Set the preferred batch size for batch prediction.
c double 1 Set the complexity constant C.
build_calibration_models boolean false Set whether to fit calibration models to SVM outputs.
checks_turned_off boolean false Disable all checks.
do_not_check_capabilities boolean false Enable capability-check.
epsilon double 1.00E-12 Set the epsilon for round-off error.
filter_type enum [filter_none, filter_normalize, filter_standardize] filter_normalize Set how the training data will be transformed.
kernel enum [PolyKernel, NormalizedPolyKernel, PrecomputedKernelMatrixKernel, Puk, RBFKernel, StringKernel] PolyKernel Specify the Kernel to use.
num_decimal_places int 2 Set the number of decimal places.
num_folds int -1 Set the number of folds for the internal cross-validation.
random_seed int 1 Set the random number seed.
tolerance_parameter double 1.00E-03 Set the tolerance parameter.
Decision Stump
Option Name Data Type Default Value Description
batch_size int 100 Set the preferred batch size for batch prediction.
do_not_check_capabilities boolean false Enable capability-check.
num_decimal_places int 2 Set the number of decimal places.
Random Forests
Option Name Data Type Default Value Description
bag_size_percent int 100 Specify the size of each bag, as a percentage of the training set size.
batch_size int 100 Set the preferred batch size for batch prediction.
break_ties_randomly boolean false Set whether to break ties randomly when several attributes look equally good.
calc_out_of_bag boolean false Set whether to calculate the out-of-bag error.
compute_attribute_importance boolean false Set whether to compute and output attribute importance (mean impurity decrease method).
do_not_check_capabilities boolean false Enable capability-check.
max_depth int 0 Set the maximum depth of the tree, 0 for unlimited.
num_decimal_places int 2 Set the number of decimal places.
num_execution_slots int 1 Set the number of execution slots.
num_features int 0 Set the number of attributes to randomly investigate.
num_iterations int 100 Set the number of iterations.
seed int 1 Set the seed for the random number generator.
store_out_of_bag_predictions boolean false Set whether to store out-of-bag predictions in the internal evaluation object.
Multilayer Perceptron
Option Name Data Type Default Value Description
batch_size int 100 Set the preferred batch size for batch prediction.
do_not_check_capabilities boolean false Enable capability-check.
learning_rate double 0.3 Set the learning rate for the backward propagation. The value should be between 0 - 1.
momentum double 0.2 Set the momentum rate for the backward propagation algorithm. The value should be between 0 - 1.
num_decimal_places int 2 Set the number of decimal places.
seed int 0 Set the value used to seed the random number generator.
validation_set_size int 0 Set the percentage size of the validation set to use to terminate training. The value should be between 0 - 1.
validation_threshold int 20 Set the number of consecutive increases of error allowed for validation testing before training terminates.

Examples