ibm-research/WatsonxDocsQARetrieval huggingface dataset:
# Mining for text links #

The Text Link Analysis (TLA) node adds pattern\-matching technology to text mining's concept extraction in order to identify relationships between the concepts in the text data based on known patterns\. These relationships can describe how a customer feels about a product, which companies are doing business together, or even the relationships between genes or pharmaceutical agents\.

![Text Link Analysis node](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/ta_tla.png)

For example, extracting your competitor’s product name may not be interesting enough to you\. Using this node, you could also learn how people feel about this product, if such opinions exist in the data\. The relationships and associations are identified and extracted by matching known patterns to your text data\.

You can use the TLA pattern rules inside certain resource templates shipped with Text Analytics or create/edit your own\. Pattern rules are made up of macros, word lists, and word gaps to form a Boolean query, or rule, that is compared to your input text\. Whenever a TLA pattern rule matches text, this text can be extracted as a TLA result and restructured as output data\.

The Text Link Analysis node offers a more direct way to identify and extract TLA pattern results from your text and then add the results to the dataset in the flow\. But the Text Link Analysis node is not the only way in which you can perform text link analysis\. You can also use a Text Analytics Workbench session in the Text Mining modeling node\.

In the Text Analytics Workbench, you can explore the TLA pattern results and use them as category descriptors and/or to learn more about the results using drill\-down and graphs\. In fact, using the Text Mining node to extract TLA results is a great way to explore and fine\-tune templates to your data for later use directly in the TLA node\.

The output can be represented in up to 6 slots, or parts\.

You can find this node under the Text Analytics section of the node palette\.

Requirements\. The Text Link Analysis node accepts text data read into a field using an Import node\.

Strengths\. The Text Link Analysis node goes beyond basic concept extraction to provide information about the relationships *between* concepts, as well as related opinions or qualifiers that may be revealed in the data\.

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# Time Intervals node #

Use the Time Intervals node to specify intervals and derive a new time field for estimating or forecasting\. A full range of time intervals is supported, from seconds to years\.

Use the node to derive a new time field\. The new field has the same storage type as the input time field you chose\. The node generates the following items:

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 *  The field specified in the node properties as the  Time Field, along with the chosen prefix/suffix\. By default the prefix is `$TI_`\.
 *  The fields specified in the node properties as the  Dimension fields\.
 *  The fields specified in the node properties as the  Fields to aggregate\.

<!-- </ul> -->

You can also generate a number of extra fields, depending on the selected interval or period (such as the minute or second within which a measurement falls)\.

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# Time Plot node #

Time Plot nodes allow you to view one or more time series plotted over time\. The series you plot must contain numeric values and are assumed to occur over a range of time in which the periods are uniform\.

Figure 1\. Plotting sales of men's and women's clothing and jewelry over time

![Plotting sales of men's and women's clothing and jewelry over time](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/graphs_timeseries_jewelry_sales.jpg)
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# TLA node output #

After running a Text Link Analysis node, the data is restructured\. It's important to understand the way text mining restructures your data\.

If you desire a different structure for data mining, you can use nodes on the Field Operations palette to accomplish this\. For example, if you're working with data in which each row represents a text record, then one row is created for each pattern uncovered in the source text data\. For each row in the output, there are 15 fields:

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 *  Six fields ( Concept\#, such as  Concept1,  Concept2, \.\.\., and  Concept6) represent any concepts found in the pattern match
 *  Six fields ( Type\#, such as  Type1,  Type2, \.\.\., and  Type6) represent the type for each concept
 *  Rule Name represents the name of the text link rule used to match the text and produce the output
 *  A field using the name of the ID field you specified in the node and representing the record or document ID as it was in the input data
 *  Matched Text represents the portion of the text data in the original record or document that was matched to the TLA pattern

<!-- </ul> -->

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# Linguistic resources #

SPSS Modeler uses an extraction process that relies on linguistic resources\. These resources serve as the basis for how to process the text data and extract information to get the concepts, types, and sometimes patterns\.

The linguistic resources can be divided into different types:

Category sets
:   Categories are a group of closely related ideas and patterns that the text data is assigned to through a scoring process\.

Libraries
:   Libraries are used as building blocks for both TAPs and templates\. Each library is made up of several dictionaries, which are used to define and manage terms, synonyms, and exclude lists\. While libraries are also delivered individually, they are prepackaged together in templates and TAPs\.

Templates
:   Templates are made up of a set of libraries and some advanced linguistic and nonlinguistic resources\. These resources form a specialized set that is adapted to a particular domain or context, such as product opinions\.

Text analysis packages (TAP)
:   A text analysis package is a predefined template that is bundled with one or more sets of predefined category sets\. TAPs bundle together these resources so that the categories and the resources that were used to generate them are both stored together and reusable\.

Note: During extraction, some compiled internal resources are also used\. These compiled resources contain many definitions that complement the types in the Core library\. These compiled resources cannot be edited\.
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# Advanced linguistic settings #

When you build categories, you can select from a number of advanced linguistic category building techniques such as concept inclusion and semantic networks (English text only)\. These techniques can be used individually or in combination with each other to create categories\.

Keep in mind that because every dataset is unique, the number of methods and the order in which you apply them may change over time\. Since your text mining goals may be different from one set of data to the next, you may need to experiment with the different techniques to see which one produces the best results for the given text data\. None of the automatic techniques will perfectly categorize your data; therefore we recommend finding and applying one or more automatic techniques that work well with your data\.

The following advanced settings are available for the  Use linguistic techniques to build categories option in the category settings\.

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# The Concepts tab #

In the Text Analytics Workbench, you can use the  Concepts tab to create and explore concepts as well as explore and tweak the extraction results\.

Concepts are the most basic level of extraction results available to use as building blocks, called descriptors, for your categories\. Categories are a group of closely related ideas and patterns to which documents and records are assigned through a scoring process\.

Text mining is an iterative process in which extraction results are reviewed according to the context of the text data, fine\-tuned to produce new results, and then reevaluated\. Extraction results can be refined by modifying the linguistic resources\. To simplify the process of fine\-tuning your linguistic resources, you can perform common dictionary tasks directly from the  Concepts tab\. You can fine\-tune other linguistic resources directly from the  Resource editor tab\.

Figure 1\. Concepts tab

![Concepts tab](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tmwb_conceptview.png)
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# The Resource editor tab #

Text Analytics rapidly and accurately captures key concepts from text data by using an extraction process\. This process relies on linguistic resources to dictate how large amounts of unstructured, textual data should be analyzed and interpreted\.

You can use the  Resource editor tab to view the linguistic resources used in the extraction process\. These resources are stored in the form of templates and libraries, which are used to extract concepts, group them under types, discover patterns in the text data, and other processes\. Text Analytics offers several preconfigured resource templates, and in some languages, you can also use the resources in text analysis packages\.

Figure 1\. Resource editor tab

![Resource editor tab in the Text Analytics Workbench](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tmwb_resourceeditor.png)
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# Text Analytics Workbench #

From a Text Mining modeling node, you can choose to launch an interactive Text Analytics Workbench session when your flow runs\. In this workbench, you can extract key concepts from your text data, build categories, explore patterns in text link analysis, and generate category models\.

You can use the Text Analytics Workbench to explore the results and tune the configuration for the node\.

Concepts
:   Concepts are the key words and phrases identified and extracted from your text data, also referred to as extraction results\. These concepts are grouped into types\. You can use these concepts to explore your data and create your categories\. You can manage the concepts on the  Concepts tab\.

Text links
:   If you have text link analysis (TLA) pattern rules in your linguistic resources or are using a resource template that already has some TLA rules, you can extract patterns from your text data\. These patterns can help you uncover interesting relationships between concepts in your data\. You can also use these patterns as descriptors in your categories\. You can manage these on the  Text links tab\.

Categories
:   Using descriptors (such as extraction results, patterns, and rules) as a definition, you can manually or automatically create a set of categories\. Documents and records are assigned to these categories based on whether or not they contain a part of the category definition\. You can manage categories on the  Categories tab\.

Resources
:   The extraction process relies on a set of parameters and definitions from linguistic resources to govern how text is extracted and handled\. These are managed in the form of templates and libraries on the  Resource editor tab\.

Figure 1\. Text Analytics Workbench

![Text Analytics Workbench](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/ta_taw.png)
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# Setting options #

You can access settings in various panes of the Text Analytics Workbench, such as extraction settings for concepts\.

On the  Concepts,  Text links, and  Categories tabs, categories are built from descriptors derived from either types or type patterns\. In the table, you can select the individual types or patterns to include in the category building process\. A description of all settings on each tab follows\.

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# The Categories tab #

In the Text Analytics Workbench, you can use the  Categories tab to create and explore categories as well as tweak the extraction results\.

Extraction results can be refined by modifying the linguistic resources, which you can do directly from the  Categories tab\.

Figure 1\. Categories tab

![Categories tab](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tmwb_categoryview.png)
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# The Text links tab #

On the  Text links tab, you can build and explore text link analysis patterns found in your text data\. Text link analysis (TLA) is a pattern\-matching technology that enables you to define TLA rules and compare them to actual extracted concepts and relationships found in your text\.

Patterns are most useful when you are attempting to discover relationships between concepts or opinions about a particular subject\. Some examples include wanting to extract opinions on products from survey data, genomic relationships from within medical research papers, or relationships between people or places from intelligence data\.

After you've extracted some TLA patterns, you can explore them and even add them to categories\. To extract TLA results, there must be some TLA rules defined in the resource template or libraries you're using\.

With no type patterns selected, you can click the  Settings icon to change the extraction settings\. For details, see [Setting options](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/tmwb_intro_options.html)\. You can also click the  Filter icon to filter the type patterns that are displayed

Figure 1\. Text links view

![Text links view](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tmwb_tlaview.png)
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# Transform node #

Normalizing input fields is an important step before using traditional scoring techniques such as regression, logistic regression, and discriminant analysis\. These techniques carry assumptions about normal distributions of data that may not be true for many raw data files\. One approach to dealing with real\-world data is to apply transformations that move a raw data element toward a more normal distribution\. In addition, normalized fields can easily be compared with each other—for example, income and age are on totally different scales in a raw data file but, when normalized, the relative impact of each can be easily interpreted\.

The Transform node provides an output viewer that enables you to perform a rapid visual assessment of the best transformation to use\. You can see at a glance whether variables are normally distributed and, if necessary, choose the transformation you want and apply it\. You can pick multiple fields and perform one transformation per field\.

After selecting the preferred transformations for the fields, you can generate Derive or Filler nodes that perform the transformations and attach these nodes to the flow\. The Derive node creates new fields, while the Filler node transforms the existing ones\.

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# Tree\-AS node #

The Tree\-AS node can be used with data in a distributed environment\. With this node, you can choose to build decision trees using either a CHAID or Exhaustive CHAID model\.

CHAID, or Chi\-squared Automatic Interaction Detection, is a classification method for building decision trees by using chi\-square statistics to identify optimal splits\.

CHAID first examines the crosstabulations between each of the input fields and the outcome, and tests for significance using a chi\-square independence test\. If more than one of these relations is statistically significant, CHAID will select the input field that is the most significant (smallest `p` value)\. If an input has more than two categories, these are compared, and categories that show no differences in the outcome are collapsed together\. This is done by successively joining the pair of categories showing the least significant difference\. This category\-merging process stops when all remaining categories differ at the specified testing level\. For nominal input fields, any categories can be merged; for an ordinal set, only contiguous categories can be merged\.

Exhaustive CHAID is a modification of CHAID that does a more thorough job of examining all possible splits for each predictor but takes longer to compute\.

Requirements\. Target and input fields can be continuous or categorical; nodes can be split into two or more subgroups at each level\. Any ordinal fields used in the model must have numeric storage (not string)\. If necessary, use the Reclassify node to convert them\.

Strengths\. CHAID can generate nonbinary trees, meaning that some splits have more than two branches\. It therefore tends to create a wider tree than the binary growing methods\. CHAID works for all types of inputs, and it accepts both case weights and frequency variables\.

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# Time Series node #

The Time Series node can be used with data in either a local or distributed environment\. With this node, you can choose to estimate and build exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), or multivariate ARIMA (or transfer function) models for time series, and produce forecasts based on the time series data\.

Exponential smoothing is a method of forecasting that uses weighted values of previous series observations to predict future values\. As such, exponential smoothing is not based on a theoretical understanding of the data\. It forecasts one point at a time, adjusting its forecasts as new data come in\. The technique is useful for forecasting series that exhibit trend, seasonality, or both\. You can choose from various exponential smoothing models that differ in their treatment of trend and seasonality\.

ARIMA models provide more sophisticated methods for modeling trend and seasonal components than do exponential smoothing models, and, in particular, they allow the added benefit of including independent (predictor) variables in the model\. This involves explicitly specifying autoregressive and moving average orders as well as the degree of differencing\. You can include predictor variables and define transfer functions for any or all of them, as well as specify automatic detection of outliers or an explicit set of outliers\.

Note: In practical terms, ARIMA models are most useful if you want to include predictors that might help to explain the behavior of the series that is being forecast, such as the number of catalogs that are mailed or the number of hits to a company web page\. Exponential smoothing models describe the behavior of the time series without attempting to understand why it behaves as it does\. For example, a series that historically peaks every 12 months will probably continue to do so even if you don't know why\.

An  Expert Modeler option is also available, which attempts to automatically identify and estimate the best\-fitting ARIMA or exponential smoothing model for one or more target variables, thus eliminating the need to identify an appropriate model through trial and error\. If in doubt, use the Expert Modeler option\.

If predictor variables are specified, the Expert Modeler selects those variables that have a statistically significant relationship with the dependent series for inclusion in ARIMA models\. Model variables are transformed where appropriate using differencing and/or a square root or natural log transformation\. By default, the Expert Modeler considers all exponential smoothing models and all ARIMA models and picks the best model among them for each target field\. You can, however, limit the Expert Modeler only to pick the best of the exponential smoothing models or only to pick the best of the ARIMA models\. You can also specify automatic detection of outliers\.

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# Streaming Time Series node #

You use the Streaming Time Series node to build and score time series models in one step\. A separate time series model is built for each target field, however model nuggets are not added to the generated models palette and the model information cannot be browsed\.

Methods for modeling time series data require a uniform interval between each measurement, with any missing values indicated by empty rows\. If your data does not already meet this requirement, you need to transform values as needed\.

Other points of interest regarding Time Series nodes:

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 *  Fields must be numeric\.
 *  Date fields cannot be used as inputs\.
 *  Partitions are ignored\.

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The Streaming Time Series node estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function) models for time series and produces forecasts based on the time series data\. Also available is an Expert Modeler, which attempts to automatically identify and estimate the best\-fitting ARIMA or exponential smoothing model for one or more target fields\.

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# TwoStep cluster node #

The TwoStep Cluster node provides a form of cluster analysis\. It can be used to cluster the dataset into distinct groups when you don't know what those groups are at the beginning\. As with Kohonen nodes and K\-Means nodes, TwoStep Cluster models do *not* use a target field\. Instead of trying to predict an outcome, TwoStep Cluster tries to uncover patterns in the set of input fields\. Records are grouped so that records within a group or cluster tend to be similar to each other, but records in different groups are dissimilar\.

TwoStep Cluster is a two\-step clustering method\. The first step makes a single pass through the data, during which it compresses the raw input data into a manageable set of subclusters\. The second step uses a hierarchical clustering method to progressively merge the subclusters into larger and larger clusters, without requiring another pass through the data\. Hierarchical clustering has the advantage of not requiring the number of clusters to be selected ahead of time\. Many hierarchical clustering methods start with individual records as starting clusters and merge them recursively to produce ever larger clusters\. Though such approaches often break down with large amounts of data, TwoStep's initial preclustering makes hierarchical clustering fast even for large datasets\.

Note: The resulting model depends to a certain extent on the order of the training data\. Reordering the data and rebuilding the model may lead to a different final cluster model\.

Requirements\. To train a TwoStep Cluster model, you need one or more fields with the role set to `Input`\. Fields with the role set to `Target`, `Both`, or `None` are ignored\. The TwoStep Cluster algorithm does not handle missing values\. Records with blanks for any of the input fields will be ignored when building the model\.

Strengths\. TwoStep Cluster can handle mixed field types and is able to handle large datasets efficiently\. It also has the ability to test several cluster solutions and choose the best, so you don't need to know how many clusters to ask for at the outset\. TwoStep Cluster can be set to automatically exclude outliers, or extremely unusual cases that can contaminate your results\.

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# TwoStep\-AS cluster node #

TwoStep Cluster is an exploratory tool that is designed to reveal natural groupings (or clusters) within a data set that would otherwise not be apparent\. The algorithm that is employed by this procedure has several desirable features that differentiate it from traditional clustering techniques\.

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 *  Handling of categorical and continuous variables\. By assuming variables to be independent, a joint multinomial\-normal distribution can be placed on categorical and continuous variables\.
 *  Automatic selection of number of clusters\. By comparing the values of a model\-choice criterion across different clustering solutions, the procedure can automatically determine the optimal number of clusters\.
 *  Scalability\. By constructing a cluster feature (CF) tree that summarizes the records, the TwoStep algorithm can analyze large data files\.

<!-- </ul> -->

For example, retail and consumer product companies regularly apply clustering techniques to information that describes their customers' buying habits, gender, age, income level, and other attributes\. These companies tailor their marketing and product development strategies to each consumer group to increase sales and build brand loyalty\.

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# Type node #

You can specify field properties in a Type node\.

The following main properties are available\.

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 *  Field\. Specify value and field labels for data in  watsonx\.ai\. For example, field metadata imported from a data asset can be viewed or modified here\. Similarly, you can create new labels for fields and their values\.
 *  Measure\. This is the measurement level, used to describe characteristics of the data in a given field\. If all the details of a field are known, it's called fully instantiated\. Note: The measurement level of a field is different from its storage type, which indicates whether the data is stored as strings, integers, real numbers, dates, times, timestamps, or lists\.
 *  Role\. Used to tell modeling nodes whether fields will be  Input (predictor fields) or  Target (predicted fields) for a machine\-learning process\.  Both and  None are also available roles, along with  Partition, which indicates a field used to partition records into separate samples for training, testing, and validation\. The value  Split specifies that separate models will be built for each possible value of the field\.
 *  Value mode\. Use this column to specify options for reading data values from the dataset, or use the  Specify option to specify measurement levels and values\.
 *  Values\. With this column, you can specify options for reading data values from the data set, or specify measurement levels and values separately\. You can also choose to pass fields without reading their values\. You can't amend the cell in this column if the corresponding  Field entry contains a list\.
 *  Check\. With this column, you can set options to ensure that field values conform to the specified values or ranges\. You can't amend the cell in this column if the corresponding  Field entry contains a list\.

<!-- </ul> -->

Click the  Edit (gear) icon next to each row to open additional options\.

Tip: Icons in the Type node properties quickly indicate the data type of each field, such as string, date, double integer, or hashtag\.

Figure 1\. New Type node icons

![New Type node icons](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/spss_typenode_icons.png)
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# Checking type values #

Turning on the  Check option for each field examines all values in that field to determine whether they comply with the current type settings or the values that you've specified\. This is useful for cleaning up datasets and reducing the size of a dataset within a single operation\.

The  Check column in the Type node determines what happens when a value outside of the type limits is discovered\. To change the check settings for a field, use the drop\-down list for that field in the  Check column\. To set the check settings for all fields, select the check box for the top\-level  Field column heading\. Then use the top\-level drop\-down above the  Check column\.

The following check options are available:

None\. Values will be passed through without checking\. This is the default setting\.

Nullify\. Change values outside of the limits to the system null (`$null$`)\.

Coerce\. Fields whose measurement levels are fully instantiated will be checked for values that fall outside the specified ranges\. Unspecified values will be converted to a legal value for that measurement level using the following rules:

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 *  For flags, any value other than the true and false value is converted to the false value
 *  For sets (nominal or ordinal), any unknown value is converted to the first member of the set's values
 *  Numbers greater than the upper limit of a range are replaced by the upper limit
 *  Numbers less than the lower limit of a range are replaced by the lower limit
 *  Null values in a range are given the midpoint value for that range

<!-- </ul> -->

Discard\. When illegal values are found, the entire record is discarded\.

Warn\. The number of illegal items is counted and reported in the flow properties dialog when all of the data has been read\.

Abort\. The first illegal value encountered terminates the running of the flow\. The error is reported in the flow properties dialog\.

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# Converting continuous data #

Treating categorical data as continuous can have a serious impact on the quality of a model, especially if it's the target field (for example, producing a regression model rather than a binary model)\. To prevent this, you can convert integer ranges to categorical types such as `Ordinal` or `Flag`\.

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1.  Double\-click a Type node to open its properties\. Expand the Type Operations section\.
2.  Specify a value for  Set continuous integer field to ordinal if range less than or equal to\.
3.  Click  Apply to convert the affected ranges\.
4.  If desired, you can also specify a value for  Set categorical fields to None if they exceed this many values to automatically ignore large sets with many members\.

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# Setting field format options #

With the  FORMAT settings in the Type and Table nodes you can specify formatting options for current or unused fields\.

Under each formatting type, click  Add Columns and add one or more fields\. The field name and format setting will be displayed for each field you select\. Then click the gear icon to specify formatting options\.

The following formatting options are available on a per\-field basis:

Date format\. Select a date format to use for date storage fields or when strings are interpreted as dates by CLEM date functions\.

Time format\. Select a time format to use for time storage fields or when strings are interpreted as times by CLEM time functions\.

Number format\. You can choose from standard (`####.###`), scientific (`#.###E+##`), or currency display formats (`$###.##`)\.

Decimal symbol\. Select either a comma (`,`) or period (`.`) as the decimal separator\.

Number grouping symbol\. For number display formats, select the symbol used to group values (for example, the comma in `3,000.00`)\. Options include none, period, comma, space, and locale\-defined (in which case the default for the current locale is used)\.

Decimal places (standard, scientific, currency, export)\. For number display formats, specify the number of decimal places to use when displaying real numbers\. This option is specified separately for each display format\. Note that the  Export decimal places setting only applies to flat file exports\. The number of decimal places exported by the XML Export node is always 6\.

Justify\. Specifies how the values should be justified within the column\. The default setting is  Auto, which left\-justifies symbolic values and right\-justifies numeric values\. You can override the default by selecting left, right, or center\.

Column width\. By default, column widths are automatically calculated based on the values of the field\. You can specify a custom width, if needed\.

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# Setting the field role #

A field's role controls how it's used in model building—for example, whether a field is an input or target (the thing being predicted)\.

Note: The Partition, Frequency, and Record ID roles can each be applied to a single field only\.

The following roles are available:

Input\. The field is used as an input to machine learning (a predictor field)\.

Target\. The field is used as an output or target for machine learning (one of the fields that the model will try to predict)\.

Both\. The field is used as both an input and an output by the Apriori node\. All other modeling nodes will ignore the field\.

None\. The field is ignored by machine learning\. Fields whose measurement level is set to  Typeless are automatically set to  None in the  Role column\.

Partition\. Indicates a field used to partition the data into separate samples for training, testing, and (optional) validation purposes\. The field must be an instantiated set type with two or three possible values (as defined in the advanced settings by clicking the gear icon)\. The first value represents the training sample, the second represents the testing sample, and the third (if present) represents the validation sample\. Any additional values are ignored, and flag fields can't be used\. Note that to use the partition in an analysis, partitioning must be enabled in the node settings of the appropriate model\-building or analysis node\. Records with null values for the partition field are excluded from the analysis when partitioning is enabled\. If you defined multiple partition fields in the flow, you must specify a single partition field in the node settings for each applicable modeling node\. If a suitable field doesn't already exist in your data, you can create one using a Partition node or Derive node\. See [Partition node](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/partition.html) for more information\.

Split\. (Nominal, ordinal, and flag fields only\.) Specifies that a model is built for each possible value of the field\.

Frequency\. (Numeric fields only\.) Setting this role enables the field value to be used as a frequency weighting factor for the record\. This feature is supported by C&R Tree, CHAID, QUEST, and Linear nodes only; all other nodes ignore this role\. Frequency weighting is enabled by means of the  Use frequency weight option in the node settings of those modeling nodes that support the feature\.

Record ID\. The field is used as the unique record identifier\. This feature is ignored by most nodes; however, it's supported by Linear models\.

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# Viewing and setting information about types #

From the Type node, you can specify field metadata and properties that are invaluable to modeling and other work\.

These properties include:

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 *  Specifying a usage type, such as range, set, ordered set, or flag, for each field in your data
 *  Setting options for handling missing values and system nulls
 *  Setting the role of a field for modeling purposes
 *  Specifying values for a field and options used to automatically read values from your data
 *  Specifying value labels

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# What is instantiation? #

Instantiation is the process of reading or specifying information, such as storage type and values for a data field\. To optimize system resources, instantiating is a user\-directed process—you tell the software to read values by running data through a Type node\.

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 *  Data with unknown types is also referred to as uninstantiated\. Data whose storage type and values are unknown is displayed in the  Measure column of the Type node settings as  Typeless\.
 *  When you have some information about a field's storage, such as string or numeric, the data is called partially instantiated\.  Categorical or  Continuous are partially instantiated measurement levels\. For example,  Categorical specifies that the field is symbolic, but you don't know whether it's nominal, ordinal, or flag\.
 *  When all of the details about a type are known, including the values, a fully instantiated measurement level—nominal, ordinal, flag, or continuous—is displayed in this column\. Note that the continuous type is used for both partially instantiated and fully instantiated data fields\. Continuous data can be either integers or real numbers\.

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When a data flow with a Type node runs, uninstantiated types immediately become partially instantiated, based on the initial data values\. After all of the data passes through the node, all data becomes fully instantiated unless values were set to  Pass\. If the flow run is interrupted, the data will remain partially instantiated\. After the Types settings are instantiated, the values of a field are static at that point in the flow\. This means that any upstream changes will not affect the values of a particular field, even if you rerun the flow\. To change or update the values based on new data or added manipulations, you need to edit them in the Types settings or set the value for a field to  Read or  Extend\.

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# Geospatial measurement sublevels #

The Geospatial measurement level, which is used with the List storage type, has six sublevels that are used to identify different types of geospatial data\.

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 *  Point\. Identifies a specific location (for example, the center of a city)\.
 *  Polygon\. A series of points that identifies the single boundary of a region and its location (for example, a county)\.
 *  LineString\. Also referred to as a Polyline or just a Line, a LineString is a series of points that identifies the route of a line\. For example, a LineString might be a fixed item, such as a road, river, or railway; or the track of something that moves, such as an aircraft's flight path or a ship's voyage\.
 *  MultiPoint\. Used when each row in your data contains multiple points per region\. For example, if each row represents a city street, the multiple points for each street can be used to identify every street lamp\.
 *  MultiPolygon\. Used when each row in your data contains several polygons\. For example, if each row represents the outline of a country, the US can be recorded as several polygons to identify the different areas such as the mainland, Alaska, and Hawaii\.
 *  MultiLineString\. Used when each row in your data contains several lines\. Because lines cannot branch, you can use a MultiLineString to identify a group of lines (for example, data such as the navigable waterways or the railway network in each country)\.

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# Defining missing values #

In the Type node settings, select the desired field in the table and then click the gear icon at the end of its row\. Missing values settings are available in the window that appears\.

Select  Define missing values to define missing value handing for this field\. Here you can define explicit values to be considered as missing values for this field, or this can also be accomplished by means of a downstream Filler node\.

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# Data values #

Using the  Value mode column in the Type node settings, you can read values automatically from the data, or you can specify measures and values\.

The options available in the  Value mode drop\-down provide instructions for auto\-typing, as shown in the following table\.

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Table 1\. Instructions for auto\-typing

| Option    | Function                                                              |
| --------- | --------------------------------------------------------------------- |
| `Read`    | Data is read when the node runs\.                                     |
| `Extend`  | Data is read and appended to the current data (if any exists)\.       |
| `Pass`    | No data is read\.                                                     |
| `Current` | Keep current data values\.                                            |
| `Specify` | You can click the gear icon at the end of the row to specify values\. |

<!-- </table "summary="" class="defaultstyle" "> -->

Running a Type node or clicking  Read Values auto\-types and reads values from your data source based on your selection\. You can also specify these values manually by using the  Specify option and clicking the gear icon at the end of a row\.

After you make changes for fields in the Type node, you can reset value information using the following buttons:

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 *  Using the  Clear all values button, you can clear changes to field values made in this node (non\-inherited values) and reread values from upstream operations\. This option is useful for resetting changes that you may have made for specific fields upstream\.
 *  Using the  Clear values button, you can reset values for all fields read into the node\. This option effectively sets the  Value mode column to `Read` for all fields\. This option is useful for resetting values for all fields and rereading values and measurement levels from upstream operations\.

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# Specifying values for collection data #

Collection fields display non\-geospatial data that's in a list\.

The only item you can set for the  Collection measurement level is the  List measure\. By default, this measure is set to  Typeless, but you can select another value to set the measurement level of the elements within the list\. You can choose one of the following options:

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 *  Typeless
 *  Continuous
 *  Nominal
 *  Ordinal
 *  Flag

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# Specifying values and labels for continuous data #

The  Continuous measurement level is for numeric fields\.

There are three storage types for continuous data:

<!-- <ul> -->

 *  Real
 *  Integer
 *  Date/Time

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The same settings are used to edit all continuous fields\. The storage type is displayed for reference only\. Select the desired field in the Type node settings and then click the gear icon at the end of its row\.

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# Specifying values for a flag #

Use flag fields to display data that has two distinct values\. The storage types for flags can be string, integer, real number, or date/time\.

True\. Specify a flag value for the field when the condition is met\.

False\. Specify a flag value for the field when the condition is not met\.

Labels\. Specify labels for each value in the flag field\. These labels appear in a variety of locations, such as graphs, tables, output, and model browsers\.

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# Specifying values for geospatial data #

Geospatial fields display geospatial data that's in a list\. For the  Geospatial measurement level, you can use various options to set the measurement level of the elements within the list\.

Type\. Select the measurement sublevel of the geospatial field\. The available sublevels are determined by the depth of the list field\. The defaults are: Point (zero depth), LineString (depth of one), and Polygon (depth of one)\.

For more information about sublevels, see [Geospatial measurement sublevels](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/type_levels_geo.html)\.

Coordinate system\. This option is only available if you changed the measurement level to  Geospatial from a non\-geospatial level\. To apply a coordinate system to your geospatial data, select this option\. To use a different coordinate system, click  Change\.

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# Specifying values and labels for nominal and ordinal data #

Nominal (set) and ordinal (ordered set) measurement levels indicate that the data values are used discretely as a member of the set\. The storage types for a set can be string, integer, real number, or date/time\.

The following controls are unique to nominal and ordinal fields\. You can use them to specify values and labels\. Select the desired field in the Type node settings and then click the gear icon at the end of its row\.

Values and Labels\. You can specify values based on your knowledge of the current field\. You can enter expected values for the field and check the dataset's conformity to these values using the  Check options\. And you can specify lables for each value in the set\. Thse labels appear in a variety of locations, such as graphs, tables, output, and model browsers\.

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# Web node #

Web nodes show the strength of relationships between values of two or more symbolic fields\. The graph displays connections using varying types of lines to indicate connection strength\. You can use a Web node, for example, to explore the relationship between the purchase of various items at an e\-commerce site or a traditional retail outlet\.

Figure 1\. Web graph showing relationships between the purchase of grocery items

![Web graph showing relationships between the purchase of grocery items](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/graphs_web_generated.jpg)
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# XGBoost\-AS node #

XGBoost© is an advanced implementation of a gradient boosting algorithm\. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier\. XGBoost is very flexible and provides many parameters that can be overwhelming to most users, so the XGBoost\-AS node in  Watson Studio exposes the core features and commonly used parameters\. The XGBoost\-AS node is implemented in Spark\.

For more information about boosting algorithms, see the [XGBoost Tutorials](http://xgboost.readthedocs.io/en/latest/tutorials/index.html)\. ^1^

Note that the XGBoost cross\-validation function is not supported in  Watson Studio\. You can use the Partition node for this functionality\. Also note that XGBoost in  Watson Studio performs one\-hot encoding automatically for categorical variables\.

Notes:

<!-- <ul> -->

 *  On Mac, version 10\.12\.3 or higher is required for building XGBoost\-AS models\.
 *  XGBoost isn't supported on IBM POWER\.

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^1^ "XGBoost Tutorials\." *Scalable and Flexible Gradient Boosting*\. Web\. © 2015\-2016 DMLC\.

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# XGBoost Linear node #

XGBoost Linear© is an advanced implementation of a gradient boosting algorithm with a linear model as the base model\. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier\. The XGBoost Linear node in  watsonx\.ai is implemented in Python\.

For more information about boosting algorithms, see the [XGBoost Tutorials](http://xgboost.readthedocs.io/en/latest/tutorials/index.html)\. ^1^

Note that the XGBoost cross\-validation function is not supported in  watsonx\.ai\. You can use the Partition node for this functionality\. Also note that XGBoost in  watsonx\.ai performs one\-hot encoding automatically for categorical variables\.

^1^ "XGBoost Tutorials\." *Scalable and Flexible Gradient Boosting*\. Web\. © 2015\-2016 DMLC\.

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# XGBoost Tree node #

XGBoost Tree© is an advanced implementation of a gradient boosting algorithm with a tree model as the base model\. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier\. XGBoost Tree is very flexible and provides many parameters that can be overwhelming to most users, so the XGBoost Tree node in  watsonx\.ai exposes the core features and commonly used parameters\. The node is implemented in Python\.

For more information about boosting algorithms, see the [XGBoost Tutorials](http://xgboost.readthedocs.io/en/latest/tutorials/index.html)\. ^1^

Note that the XGBoost cross\-validation function is not supported in  watsonx\.ai\. You can use the Partition node for this functionality\. Also note that XGBoost in  watsonx\.ai performs one\-hot encoding automatically for categorical variables\.

^1^ "XGBoost Tutorials\." *Scalable and Flexible Gradient Boosting*\. Web\. © 2015\-2016 DMLC\.

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