![]() If a use wants fewer false positives and negatives, they can consider other models, feature engineering, or additional data. Users can change the trade-off between false positives and false negatives by changing the threshold - the probability cutoff that splits a “yes” result from a “no” result. If the user knows from the confusion matrix that their model is likely to result in false negatives for the loan dataset, they know to either use a different model or make improvements to the model through manual tuning. For example, when a model predicts that a credit investment opportunity will result in default when it actually doesn’t (false positive), there is a very different consequence than when the lender mistakenly funds a loan that does result in a default (false negative). This information proves invaluable when using insights or predictions from the model to make real-world business decisions. They evaluate the performance of a classification model, allowing business users to determine which data their model may be unable to classify correctly. Here is an example of a confusion matrix from the DataRobot platform:Ĭonfusion matrices make it easy to tell how accurate a model’s outcomes are likely to be by exposing when the model is repeatedly confusing two classes. The rows of the matrix represent the actual labels contained in the training dataset, and the columns represent the model’s outcomes. When you train a machine learning classification model on a dataset, the resulting confusion matrix shows how accurately the model categorized each record and where it might be making errors. Training Sets, Validation Sets, and Holdout SetsĬonfusion Matrix What does Confusion Matrix Mean?.DataRobot Success Stories See how organizations like yours have realized more value from their AI initiatives.Deployment Infrastructure Choose how you want to deploy DataRobot, from managed SaaS, to private or public cloud.Platform Integrations Unify your data warehouses, ML APIs, workflow tooling, BI tools and business apps.Monitor and Measure ROI Monitor, measure and diagnose model accuracy, ROI, and bias in real-time from any hosting environment.Integrate Models Deploy and integrate any model, anywhere with multiple deployment options. ![]() Validate and Govern Models Create a centralized system of record for all models, test, approve, and automate compliance documentation.Make Business Decisions Evaluate model performance, identify key drivers, and create customizable apps to drive decisions.Build Models Train hundreds of modeling strategies in parallel using structured and unstructured data.Prepare Modeling Data Connect data, assess data quality, engineer new features, and integrate with feature stores.Discover the DataRobot AI Platform The only fully open, end-to-end AI lifecycle platform with deep ecosystem integrations and applied AI expertise.
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