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How to evaluate a classifier in scikit-learn
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In this video, you'll learn how to properly evaluate a classification model using a variety of common tools and metrics, as well as how to adjust the performance of a classifier to best match your business objectives. I'll start by demonstrating the weaknesses of classification accuracy as an evaluation metric. I'll then discuss the confusion matrix, the ROC curve and AUC, and metrics such as sensitivity, specificity, and precision. By the end of the video, you will have a solid foundation for intelligently evaluating your own classification model.
== CONFUSION MATRIX RESOURCES ==
== ROC/AUC RESOURCES ==
== OTHER RESOURCES ==
WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS:
1) WATCH my scikit-learn video series:
2) SUBSCRIBE for more videos:
3) JOIN "Data School Insiders" to access bonus content:
4) ENROLL in my Machine Learning course:
5) LET'S CONNECT!
== CONFUSION MATRIX RESOURCES ==
== ROC/AUC RESOURCES ==
== OTHER RESOURCES ==
WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS:
1) WATCH my scikit-learn video series:
2) SUBSCRIBE for more videos:
3) JOIN "Data School Insiders" to access bonus content:
4) ENROLL in my Machine Learning course:
5) LET'S CONNECT!
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