Machine learning pipeline automation has completely revolutionized the way organizations approach data analysis and model development. Through automated pipeline creation, data scientists can rapidly extract valuable insights from diverse datasets. However, the key to unlocking the full potential of automated pipelines lies in the ability to customize, providing users with an enhanced level of control and flexibility.   In this article, we dive into the realm of machine learning pipeline automation, shedding light on how advanced settings equip users with the necessary tools to tailor pipelines according to their unique requirements.   Machine Learning Pipeline Automation (MLPA) Advance Settings The “Advanced Settings” window offers the means for customizing your automated pipeline creation.   Within the Advanced Settings window, the algorithms option offers users the ability to customize their pipelines by specifying which models should be considered or excluded. This feature provides granular control, allowing data scientists to select the most relevant algorithms based on their specific use cases and data characteristics.   When a model is selected in the “Consider” list, it becomes part of the pool of candidate models that undergo performance comparison within the pipeline. Therefore, while models in the “Consider” list are given a fair chance, the final selection process prioritizes the best-performing models based on a specified selection statistic like misclassification rate or average squared error.   In certain scenarios, users may have specific requirements where a particular model must be included in the final pipeline. For instance, in the banking industry, the interpretability of the "Regression" model makes it a preferred choice. In such cases, the “Force Include” option can be used to include the model in the final pipeline.   Note, when considering a Gradient Boosting model, the final pipeline also takes LightGBM into consideration.   Another essential customization option within the Advanced Settings window is the sampling feature. This powerful tool allows users to define how their data should be sampled during the pipeline creation process. This functionality enables the efficient processing of large datasets, reducing computational time and resource requirements while preserving representative data characteristics.   Users have the choice to either specify a fixed number of training rows or a percentage of the training rows. Lastly, you can access the same capabilities and much more through our public API. For additional information, please refer to the links provided here and here.   In summary, this article explores the advanced options available in SAS Viya’s Machine learning pipeline automation (MLPA). By utilizing advanced settings, users gain control and flexibility in tailoring pipelines to meet their specific needs.     Additional Resources Advance Settings documentation Automate your machine learning pipeline with SAS Viya ... View more

In this article, you will learn how Machine Learning Pipeline Automation (MLPA) in SAS Model Studio utilizes date columns when set as input in the data tab.   The difference between a good model and an excellent model is determined by how rich your modeling data is. To improve the accuracy of the model, feature extraction is one of the crucial steps. Did you know that dates are not only limited to time series modeling and you can use the features from dates in a machine learning pipeline to enrich the modeling data?   Let’s see how you can extract features such as year, month, quarter, weekday, and day from your date column and use it as input to your automated machine learning pipeline instead of rejecting dates or simply using them as raw dates.   Overview In SAS Model Studio a date variable is identified as a column with any date format for example MMDDYYY10. etc. and has a numeric type. By default, the role of such variables is set to rejected. However, a user can override this default by simply setting it as either input or ID. Once the role of a date variable is overridden, the automated machine learning pipeline will insert a custom sas code node right after the data node where all the magic will happen.   Example In this example, I am using a housing dataset that has two date variables i.e. “Claim Date” and “Effective Date” but you can use any dataset in which you would like to process the dates.       Once the role of the desired date has been set to either ‘INPUT’ or ‘ID’ and the target has been assigned, we can proceed with generating an automated machine learning pipeline. For this example, I am going to set “Claim_Date” as an input.             Create a new pipeline and select “Automatically generate the pipeline” and set the automation time limit to your desired value. I am going with the default of 15 minutes for this example.   Once the pipeline has finished running successfully, you will notice that there is a SAS Code node right after the Data node (which you won’t see if the date variable is set to rejected).         If you open the code editor of that SAS Code node, it shows the score code that has been generated automatically for you, which extracts new features from the date variable “Claim_Date” and sets the original variable to “Rejected”. This ensures that the pipeline is only using newly generated features and not the original variable to avoid any redundancy.         Let’s take a look at the output variable table in the results window. It shows below the five new variables that have been generated and the original “Claim_Date” variable has been set to the ‘REJECTED’ role.       Lastly, let’s take a look at one of the candidate models and see if any of the new features generated contributed to it.   Below is the result from the Linear regression model node and “yr_CLA1” variable which represents year information from the “Claim_Date” variable is one of the significant variables in this model.         Note, you can always add another pipeline with none of the date variables as input and do the pipeline comparison with date variables as input to see if adding these new date features improves the accuracy of your model as it is highly dependent on the type of dataset you are using.   Similarly, learn more about extracting features from text variables in this article.   Summary In this article, I have tried to showcase one of the ways how dates can be utilized in the modeling process. This technique can be used to improve the predictive performance of the models depending on the type of dataset.   It also covered an example of SAS Model Studio Automated pipeline creation, which uses a date variable to extract new features for subsequent use.   ... View more

This article will showcase how to use the Text Mining Node in SAS Model Studio and how the Text Mining node is automatically used in Model Studio’s Automated Pipeline Creation when a variable with a “text” role is detected.   Overview Text mining is the process of analyzing unstructured data e.g., free form data from the web, comment fields, and other text sources to reveal patterns and insights. By transforming the data into a structured format, a user can use that information for further analysis such as building predictive models.   The SAS Model Studio Text Mining node lets users process text information and create quantitative representations (Singular value decomposition matrix) of the text for further use like modeling. Often, by using the additional information from the text field, you might be able to improve the predictive ability of your models. One variable must contain the text and if multiple text variables are available in the data, then the Text Mining node uses the one with the largest length by default. However, a user can override this default by simply rejecting other variables with the “text” role in the Data tab. This ensures that only the desired variable is used as an input for the Text Mining node.   Examples In this demonstration, I'll show two examples of how the Text Mining node can be used in SAS Model Studio to create new quantitative features for further analysis. The first example shows using the Text Mining node in Model Studio pipeline and the second example shows how it is incorporated in Automated Pipeline Creation.   There are currently three text variables in the “Wine_Quality_Reviews” dataset and the Text Mining node will by default be using the “Description” variable for analysis because of its largest length. Description variable represents free-form, unstructured data on wine reviews. As mentioned above, it can be overridden by rejecting the remaining two text variables by using the Data tab and only keeping the one you want to use. But for this example, I will go ahead with the default.     Once the target (Varietal – which represents the variety of wine) is assigned, our next step is to add a Text Mining node under the Pipelines tab by right-clicking on the data node and selecting the Text Mining node from the “Data Mining Preprocessing” group.   When you select the Text Mining node, the Options pane will appear in the right pane.     There are several parsing options available and the image above shows the default parsing options. By selecting “Include parts of speech,” a user is telling the node to use part-of-speech tagging in the parsing process. The “Extract noun groups” option lets users extract noun groups from the chosen text variable during parsing. Similarly, the “Extract entities” option lets users extract entities. The “Stem Terms” option specifies whether to treat different terms but with the same roots as equivalent terms.   “Minimum number of document” property, as the name suggests, specifies the minimum number of documents a term should be in to be kept.   Starting Viya 4 2021.1.3, a user has the option to upload their own lists such as stop list, start list, etc. under the “Custom Lists” property. Before version Viya 4 2021.1.3, default lists are automatically included and applied for all supported languages in the Text Mining node.   Lastly, the “Topic Discovery” property lets the user either automatically determine the number of topics generated or lets the user manually specify the number of topics to be discovered.   For this example, I am using the default settings to run the Text Mining node. Looking at our Results, several windows are available like the table of topics generated, Kept Terms, and Dropped Terms. Kept Terms, and Dropped Terms tables give information on used and ignored terms respectively during the text analysis.     Notice, several terms include the plus sign which indicates stemming. that reduces the words to their word stem or root.   Let’s take a look at the Topics table output. There are 25 topics created based on groups of terms that occurred together more frequently. Each term-document pair is assigned a score for every topic. Thresholds are then used to determine whether the association is strong enough to consider whether that document or term belongs to the topic. Because of this, terms and documents can belong to multiple topics.   These 25 topics are essentially 25 new columns that are created in the output table. These output columns represent singular value decomposition (SVD) scores which are numeric (interval variables) in nature and will be used in subsequent nodes as inputs. You can also preview the output data and variables table via the Output Data tab, where COL1 to COL25 represents the new features generated by the Text Mining node.   Now just for illustration purposes, let’s add two modeling nodes (Decision tree), one following the Text Mining Node and another directly after the Data node. In the real world, you would want to add multiple models for comparison but for this example, I am going to show a comparison among these two Decision Trees to showcase how new features generated by the Text Mining node were used by the following nodes.         Once the run is complete, let’s take a look at the Model Comparison node. You'll notice that the Decision Tree node -- which was followed by the Text Mining node --- was the champion model in this case. Adding these new text features does not always guarantee performance increase, but it is worth extracting meaningful information from unstructured data to try boosting the model’s performance.   In our case, it clearly has helped improve the Decision Tree’s performance.     Looking at the results from the Decision Tree node followed by the Text Mining node, notice how some of the new features generated by the Text Mining node made it to the top of the variable importance list.     Automated Pipeline Creation In this example, let's look at how SAS Model Studio can be used to dynamically build a pipeline for you that is based on the data.   When you create a new pipeline, choose the “Automatically generate the pipeline” option.       In our case, Automated Pipeline creation will automatically detect the “text” role assigned to one of the variables and performs data preparation, model building, hyperparameter tuning, model comparisons, and model selection on your data to create a pipeline.   Let's take a look at the final pipeline generated. Note, you can always unlock the pipeline and change the node’s properties to the desired value, and then run the pipeline again for updated results.     Notice, how the Data node is followed by the Text Mining node because we had a variable with a text role available in the data. In this case, Automated Pipeline creation ran several models including the ensemble node to determine the best model. Model Comparison results show that the Gradient Boosting model (which is followed by the Imputation node in the final pipeline) is the champion model, so let's take a peek at its results.     Looking at the Gradient Boosting node (champion model) results, we can see that many of the Text Mining nodes generated features -- that were transformed by the Transformation and Imputation node in the flow -- have made it to the top of the variable importance list here as well.       Lastly, one note when you only have one input variable in the data (in addition to the target variable). If it happens to be a “text” variable, then Automated Pipeline creation would use the new generated interval features (SVD scores) from the Text Mining node for all the subsequent nodes as inputs to create a final pipeline.   Summary   This article presented the importance of gaining useful information from unstructured data using SAS Model Studio’s Text Mining node and its properties overview. It also covered examples of SAS Model Studio Automated pipeline creation, which automatically detects “text” variables and extracts new features for subsequent use.   This article is a glimpse into this neat node available in Model Studio. Hopefully, it will encourage users to take advantage of this node to further enhance their data mining and model building process.   Additional Resources Video tutorial: Gradient boosting explained Video tutorial: How to compare models in SAS Other community articles on SAS Model Studio Learn SAS Data Mining and Machine Learning ... View more