No-Code and Low-Code Machine Learning Prove to Get 90% Accurate Results

No-code/low-code machine learning experiment and got better than a 90 percent accuracy rate.

When researchers performed the first part of our no-code/low-code machine learning experiment and got better than a 90 percent accuracy rate on a model. Low-code/no-code AI tools rely on visual interfaces with drag-and-drop functions and drop-down menus for building machine-learning models.

The University of California-Irvine tried to outperform data science students' results using the easy button of Amazon Web Services' low-code and no-code tools. Sometimes these tools simply automate processes that in the past would have required more manual labor using spreadsheets. These tools are effective, accurate, and more cost-effective than finding someone who hardly knew what the heck they were doing to hand it off to them. The business side wants to accelerate low-code/no-code and IT, and security feels like they're losing control.

No-code tools:

The no-code option that AWS provides SageMaker, Canvas is intended to work hand-in-hand more with the data science approach for SageMaker Studio. But Canvas outperformed to do with the low-code approach of Studio. A Jupyter-based platform is for doing data science and machine learning experiments. Jupyter is based on Python. It is a web-based interface to a container environment that allows you to spin up kernels based on different Python implementations, depending on the task.

The Studio environment created with the Canvas link included some pre-built content providing insight into the model Canvas produced. Hyperparameters are tweaks that AutoML made to calculations by the algorithm to improve the accuracy, as well as some basic housekeeping of the SageMaker instance parameters. The relative importance of each of the columns is rated with something called SHAP values. SHAP stands for Shapley Additive exPlanations, which is a game theory. It is a really horrible acronym. It is based on the method of extracting each data feature's contribution to a change in the model output.

There are a few other figures that are of importance here statistically:

Precision: Percentage of positive instances out of the total predicted positive instances. Recall: Percentage of positive instances out of the total actual positive instances. And F1 score: It is the harmonic mean of precision and recall. This takes the contribution of both, so the higher the F1 score, the better. So a model does well in F1 score if the positive predicted are actually positives (precision) and doesn't miss out on positives and predicts them negative (recall). One drawback is that both precision and recall are given equal importance due to which, according to our application, we may need one higher than the other and the F1 score may not be the exact metric for it.

Data Wrangler:

Data Wrangler to do something about data quality. A new Data Wrangler app in the EC2 cloud. Wrangler is an interactive No-code tool for data cleaning and transformation. Spend less time formatting and more time analyzing your data. AWS Data Wrangler is an open-source Python library that enables you to focus on the transformation step of ETL by using familiar Pandas transformation commands. Hopefully, some magical data transformation would make everything better. Data quality analysis, which generated a pile of statistics about the table imported. Human effort requirements for the project are supposed to be between one and three person-days, depending on the platform used and the skill of the data scientist.

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