Automated machine learning is your easy introduction to the complex world of machine learning.
You can now use automated machine learning on real-world challenges and start learning to use machine learning at the same time.
It allows you to begin to explore machine learning without the steep learning curve traditionally associated with it.
Given machine learning is applicable to your entire workflow, from processing raw datasets to deploying ready-to-use machine learning models, it is easy to find good use cases and so get started quickly.
AutoML in Power BI
AutoM for Power BI for dataflows is a game-changer.
By creating a dataflow and selecting the desired training data AutoML automatically identifies the most relevant features and chooses the most appropriate model, fine-tuning it to your specific objectives.
This streamlined process allows Power BI to generate a comprehensive performance report, offering insights into the model’s efficacy and areas for improvement.
Prerequisites for Leveraging AutoML
Accessing AutoML’s advanced capabilities requires a Power BI Premium subscription, which is geared towards enterprise users or professional business analysts.
Inevitably, this requires a paid subscription.
This premium tier provides the advanced features that set Power BI apart from competitors like Tableau and Google Data Studio.
AutoML supports three types of supervised machine learning models:
- Binary Prediction,
- Classification, and
- Regression.
These models learn from data with a known outcome, making them ideal for applications like analyzing historical customer data to predict future behaviours or outcomes.
Setting Up Your Machine Learning Model
Creating a dataflow for machine learning begins with preparing your dataset, ideally structured within a data model.
Once your dataset is ready, the next step is to create a workspace in Power Bi with AI capability enabled.
The process to define your machine learning model in Power BI is pretty intuitive.
After selecting your dataflow, select the Apply ML model button in the Actions section, and click Add a machine learning model.
Next you specify what historical data you want to use and the outcome you want to predict.
Power BI will recommend the best model based on your data and the outcome that you are looking to achieve, for example binary outcomes for purchase decisions or classificationfor more complex tasks.
The platform guides you through selecting the most relevant data columns for model training, offering explanations for any excluded columns and allowing for manual adjustments if desired.
The final step involves naming your model and choosing the training duration, with longer training periods generally resulting in higher accuracy.
Understanding Your Model with the Validation Report
When the training is finished, Power BI provides a Validation Report which allows you to assess your model’s performance.
The report will explain to you how the model will perform and break down key features.
This report outlines key features and performance metrics, and offers a Probability Threshold slider to balance recall and precision based on specific needs, such as maximizing fraud detection in sensitive applications.
The report also highlights top predictors, offering insights into the factors that were most influential in achieving high prediction accuracy.
Additionally, a Cost-Benefit Analysis tool assists in identifying the most profitable data segments to target, enhancing decision-making and optimizing outcomes.
Deploying Your Machine Learning Model
Applying the trained model involves selecting the appropriate data table and specifying names for the new prediction columns.
This process enriches your dataset with valuable predictive insights, including a PredictionScore indicating the likelihood of achieving the target outcome and a PredictionExplanation providing detailed rationale behind each prediction.
Using AutoML To Learn Machine Learning
AutoML is a fantastic way to get started with machine learning.
It means that you can quickly start playing with and seeing the benefits of machine learning without having to spend days learning the underlying science.
If you ultimately want to be a data scientist, the availability of AutoML doesn’t mean that you don’t need to understand the detail and mathematics behind the analysis.
You’ll also need to be aware of the impact that your data selection can have as managing things like bias that can creep into models will remain a manual task for the foreseeable future.
However, I would encourage anyone who is interested in machine learning to use AutoML to ‘have a play’ and start understanding how it works and what it can do for you.
Top Do’s And Don’t When Using AutoML
AutoML is a very powerful technology in the right hands but must be used with care.
Do’s:
- AutoML is a starting point.
It probably won’t give you the ultimate solution. It is fantastic for things like quickly checking for signals in a dataset and other exploratory data analysis.
- You still need to understand how your model works.
If you can’t explain how your model works, you can be sure it’s doing exactly what you want it to.
- Don’t use AutoML for the sake of it.
AutoML still need to pass the usability and economics tests. If it doesn’t perform significantly better than simple rules of thumb or models then don’t use it. If it works but the economics don’t stack up don’t use it.
Don’ts:
- AutoML is only as good as the data you feed it.
It doesn’t mean you can skip data validation and feed it raw data
- If it doesn’t look right, it probably isn’t.
AutoML is not infallible. Stay alert and interrogate what is going on.
- Usual methods and standards apply.
You still need to document your work as normal.
Conclusion
The integration of AutoML into tools like Power BI signifies a pivotal shift towards more accessible, efficient, and powerful data analysis capabilities.
It enables users to tap into the potential of machine learning, enhancing their analytical workflows and decision-making processes.
Whether you’re a seasoned data scientist or a business analyst looking to expand your toolkit, exploring AutoML within Power BI offers a compelling opportunity to delve into the future of data science and machine learning.
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