IBM SPSS Modeler Basic Version 18.0, With Complete Solution 2023
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IBM SPSS
IBM SPSS Modeler Basic Version 18.0, With Complete Solution 2023
Surveycraft File
Reads SurveyCraft case data and metadata. Specify the name of the SurveyCraft .vq file. The internal name of this DSC is mrSCDsc.
Table
The Table node is supported by writing a temporary Analytic Server data sou...
IBM SPSS Modeler Basic Version 18.0,
With Complete Solution 2023
Surveycraft File
Reads SurveyCraft case data and metadata. Specify the name of the SurveyCraft .vq
file. The internal name of this DSC is mrSCDsc.
Table
The Table node is supported by writing a temporary Analytic Server data source
containing the results of upstream operations. The Table node then pages through the
contents of that data source.
Missing
Used to specify how missing values for the field will be handled. For more information
Restructure Node
It converts a nominal or flag field into a group of fields that can be populated with the
values of yet another field.
The IBM Cognos TM1 Source Node
Imports data from Cognos TM1 databases.
Iterations
This number is calculated automatically and cannot be edited. An iteration is created
automatically each time a distribution parameter has multiple values specified.
Derive
All Derive functions are supported, with the exception of sequence functions.Split fields
cannot be derived in the same stream that uses them as splits; you will need to create
two streams; one that derives the split field and one that uses the field as splits. A flag
field cannot be used by itself in a comparison; that is, if (flagField) then ... endif will
cause an error; the workaround is to use if (flagField=trueValue) then ... end if It is
recommended when using the (operator to specify the exponent as a real number, such
as x2.0, instead of x*2), in order to match results in Modeler
The Analytic Server Source
Enables you to run a stream on Hadoop Distributed File System (HDFS). The
information in an Analytic Server data source can come from a variety of places, such
as text files and databases.
Histogram Thumbnail
Shows a plot of the selected distribution superimposed on a histogram of the historical
data of the selected field.
Weibull
The parameter Location is an optional location parameter, which specifies where the
origin of the distribution is located
Annotations Tab
Used for all nodes, this tab offers options to rename nodes, supply a custom ToolTip,
and store a lengthy annotation.
The Fixed File Node
,Imports data from fixed-field text files—that is, files whose fields are not delimited but
start at the same position and are of a fixed length. Machine-generated or legacy data
are frequently stored in fixed-field format.
R
The R syntax in the nugget should consist of record-at-a-time operations.
Measurement
This is the measurement level, used to describe characteristics of the data in a given
field. If all of the details of a field are known, it is called fully instantiated
Matrix Node
The Matrix node allows you to create a table that shows relationships between fields. It
is most commonly used to show the relationship between two categorical fields (flag,
nominal, or ordinal), but it can also be used to show relationships between continuous
(numeric range) fields.
Metadata Provider
Survey data can be imported from a number of formats as supported by your Data
Collection Survey Reporter Developer Kit.
The User Input Node
Provides an easy way to create synthetic data—either from scratch or by altering
existing data. This is useful, for example, when you want to create a test dataset for
modeling.
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.
Modeling Node that are supporting in Modeling
GLE, Auto Classifier, Auto Numeric, Auto Cluster, Time Series, TCM, Tree-AS, C&R
Tree, Quest, CHAID, Linear, Linear-AS, Neural Net, GLE, LSVM, TwoStep-AS,Random
Trees, STP, and Association Rules, Anomaly, R.
Analysis Node
It evaluates predictive models' ability to generate accurate predictions. Analysis nodes
perform various comparisons between predicted values and actual values for one or
more model nuggets. They can also compare predictive models to each other.
Parameters
Contains the distribution parameters associated with each fitted distribution. The
parameters are displayed as parameter_name = parameter_value, with the parameters
separated by a single space. For the categorical distribution, the parameter names are
the categories and the parameter values are the associated probabilities. If the
distribution was not fitted to the historical data, the cell is empty. This column cannot be
edited.
Binning That Functionality is not Supported
Optimal binning Ranks
Tiles -> Tiling: Sum of values
Tiles -> Ties: Keep in current and Assign randomly
, Tiles ->Custom N: Values over 100, and any N value where 100 % N is not equal to
zero.
The Apriori Node
Extracts a set of rules from the data, pulling out the rules with the highest information
content. Apriori offers five different methods of selecting rules and uses a sophisticated
indexing scheme to process large data sets efficiently.
For large problems, Apriori is generally faster to train; it has no arbitrary limit on the
number of rules that can be retained, and it can handle rules with up to 32
preconditions. Apriori requires that input and output fields all be categorical but delivers
better performance because it is optimized for this type of data.
The Auto Numeric Node
Estimates and compares models for continuous numeric range outcomes using a
number of different methods. The node works in the same manner as the Auto
Classifier node, allowing you to choose the algorithms to use and to experiment with
multiple combinations of options in a single modeling pass. Supported algorithms
include neural networks, C&R Tree, CHAID, linear regression, generalized linear
regression, and support vector machines (SVM). Models can be compared based on
correlation, relative error, or number of variables used.
Output Node
The Matrix, Analysis, Data Audit, Transform, Statistics, Means, and Table nodes are
supported. Further notes on supported node functionality
Multiplot Node
Is a special type of plot that displays multiple Y fields over a single X field. The Y fields
are plotted as colored lines and each is equivalent to a Plot node with Style set to Line
and X Mode set to Sort. Multi pots are useful when you have time sequence data and
want to explore the fluctuation of several variables over time.
Simulation Evolution Node
It can evaluate a specified predicted target field and presents distribution and correlation
information about the target field.
Means
The Means node cannot produce a standard error or 95% confidence interval.
The Database Node
Can be used to import data from a variety of other packages using ODBC (Open
Database Connectivity), including Microsoft SQL Server, DB2, Oracle, and others
Reading In Mixed Data
Note that when reading in fields with numeric storage (either integer, real, time,
timestamp, or date), any non-numeric values are set to null or system missing. This is
because unlike some applications, IBM SPSS Modeler does not allow mixed storage
types within a field. To avoid this, any fields with mixed data should be read in as
strings, either by changing the storage type in the source node or in the external
application as necessary.
R Transform
The R syntax in the node should consist of record-at-a-time operations.
The Time Series Node
Estimates exponential smoothing, univariate Autoregressive Integrated Moving Average
(ARIMA), and multivariate ARIMA (or transfer function) models for time series data and
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