Ideal for Well-Defined Scenarios and Predictive Modeling
Power BI AutoML excels in solving well-defined scenarios where the problem and desired outcome are clearly defined. It is particularly well-suited for tasks such as predictive modeling, classification, and anomaly detection. For instance, it can be used to predict customer churn, identify fraudulent transactions, or optimize marketing campaigns.
While Power BI AutoML is primarily focused on solving well-defined problems, it can also be used for exploratory data analysis. By building machine learning models and analyzing their results, users can gain insights into hidden patterns and relationships within their data, leading to new understanding and opportunities for business improvement.
One of the most important features of Power BI AutoML is its ability to explain how models make predictions. This is important because it helps you to understand why your model is making the predictions that it is and to identify any potential biases or errors. There are a number of ways to explain a model in Power BI AutoML, including feature importance charts and individual prediction explanations.
When you train a model in Power BI AutoML, the tool automatically tries out different algorithms and hyperparameters to find the best combination for your data. This process can take some time, but it is worth it to get the best possible results. Once the training process is complete, you can see the performance of your model and make adjustments as needed.