On this Page

Overview

This is a Transform type Snap that builds model for a regression dataset. In the Snap's settings, you can select the target field in the dataset, algorithm, and configure parameters for the selected algorithm.

If you want to build the model on regression dataset, use Trainer -- Classification Snap instead.

Input and Output

Expected input: The regression dataset.

Expected output: A serialization of the model, and metadata which are not human-readable. Additionally, the output includes a human-readable representation of the model if the Readable checkbox is selected.

Expected upstream Snap: Any Snap that generates a regression dataset document. For example, CSV Generator, JSON Generator, or a combination of File Reader and JSON Parser.

Expected downstream Snap: Snaps that require a model input. For example, the Predictor (Regression) Snap. Alternatively, any Snap that stores the model to be used in another pipeline. For example, a combinationof JSON Formatter and File Writer.

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.

Configuring Accounts

Accounts are not used with this Snap.

Configuring Views

Input	This Snap has exactly one document input view.
Output	This Snap has exactly one document output view.
Error	This Snap has at most one document error view.

Troubleshooting

None.

Limitations and Known Issues

None.

Modes

Ultra Pipelines: Does not work in Ultra Pipelines.

Snap Settings

Label	Required. The name for the Snap. Modify this to be more specific, especially if there are more than one of the same Snap in the pipeline.
Label field	Required. The label or output field in the dataset. This must be numeric. This is the field that the model will be trained to predict. Default value: None Example: $price
Algorithm	Required. The regression algorithm to be used to build the model. There are four regression algorithms currently available: K-Nearest Neighbors Linear Regression Decision Stump Random Forests The implementations are from WEKA, an open source machine learning library in Java. Default value: K-Nearest Neighbors
Options	The parameters to be applied on the selected regression algorithm. Each algorithm has a different set of parameters to be configured in this property. If this property is left blank, the default values are applied for all the parameters. If specifying multiple parameters, separate them with a comma ",". See Options for Algorithms section below for details. Default value: None Examples: batch_size = 120 batch_size = 120, collapse_tree = true
Readable	Select this to output the model in human-readable format. When selected, a $readable field is added to the output, this displays the model in a readable format. Default value: Not selected
Page lookup error: page "Anaplan Read" not found. If you're experiencing issues please see our Troubleshooting Guide.	Page lookup error: page "Anaplan Read" not found. If you're experiencing issues please see our Troubleshooting Guide.

Best Practices

Using Type Converter Snap Upstream

In some cases, the numerical fields may be represented as a string. You can use the Type Converter Snap to convert data into appropriate types before feeding into the Trainer (Regression) Snap.

Algorithm Selection

In order to choose the best possible algorithm for your dataset, use the Cross Validator (Regression) Snap to perform k-fold cross validation on the dataset. The algorithm that produces the best accuracy is likely to be the one most suitable for your dataset. Apply the same algorithm for your dataset in the Trainer (Regression) Snap to built the model.

Options for Algorithms

Click here to expand...

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

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 the 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, KDTress, FilteredNeighbourSearch]	LinearNNSearch	Set the nearestNeighbourSearch algorithm to be used for finding nearest neighbor(s).
num_decimal_places	int	2	Set the number of decimal places.
window_size	int	0 (no window size)	Set the maximum number of instances allowed in the training pool.

Linear Regression

Option Name	Option Data Type	Default Value	Description
attribute_selection_method	enum [slection_none, selection_m5, selection_greedy]	selection_m5	Set the attribute selection method to use.
batch_size	int	100	Set the preferred batch size for batch prediction.
do_not_check_capabilities	boolean	false	Set whether not to check capabilities.
eliminate_colinear_attributes	boolean	true	Set whether to try to eliminate colinear attributes.
minimal	boolean	false	Set to conserve memory, don't keep dataset header and means/stdevs.
num_decimal_places	int	4	Set the number of decimal places.
ridge	double	1.00E-08	Set the value of the ridge 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 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 random number generator.
store_out_of_bag_predictions	boolean	false	Set whether to store out-of-bag predictions in internal evaluation object.

Example

Heating Load Prediction – Model Training

This pipeline demonstrates training a model to predict whether a weighing scale is balanced. The regression algorithm is selected based on the algorithm evaluation in the Cross Validator (Regression) Snap's example.

Download this pipeline.

Understanding the pipeline

Input

The input is generated by the CSV Generator Snap and is composed of the following fields:

Relative Compactness
Surface Area
Wall Area
Roof Area
Overall Height
Orientation
Glazing Area
Glazing Area Distribution
Heating Load

This dataset has been sourced from UCI Dataset Archive.

Objective

Use Trainer (Regression) Snap to train the model for the dataset.

Data Preparation

This input document is passed through the Type Converter Snap that is configured to automatically detect and convert the data types. In any ML pipeline, you must first analyze the input document using the Profile Snap and the Type Inspector Snap to ensure that there are no null values or that the data types are accurate. This step is skipped in this example for simplicity's sake.

Below is a preview of the output from the Type Converter Snap:

Model Training

Since the training algorithm was evaluated in the Cross Validator (Regression) Snap, the Trainer (Regression) Snap is configured with the same settings as shown below:

The output from the Snap is the model for the dataset as shown below:

The model is written into a file using the File Writer Snap which is configured as shown below: