What you would learn in Machine learning model evaluation in Python course?
For this practical course, we will concentrate on evaluating the performance of the machine learning supervised model using Python programming language.
Once a model is trained or tuned during hyperparameter tuning, it is necessary to test its performance to determine whether the model overfits or not. Based on specific needs and projects, it is essential to choose performance indicators with care. The selection of the incorrect metrics could create an unreliable model. In contrast, using the right performance indicators could lead your project to have a better value.
In this course, you're going to be taught:
Performance indicators used in regression model performance (R-squared Mean Absolute Error, Absolute Percentage Error)
Performance indicators used to evaluate binary classification models (confusion matrix precision-recall, balanced accuracy precision, ROC curve, and its size)
Performance indicators for models that use multi-class classification (accuracy, as well as precision, the balance of accuracy, macro, averaged precision)
The lessons in this course begin with a short introduction and finish with a real-world demonstration of the Python programming language and its powerful scikit-learn library. The platform that will be utilized is Jupyter which is an industry-standard in the field of data science. The entire Jupyter notebooks are available for download. Available for download.
It's part of my supervised machine Learning using Python Online Course, which means you'll have access to several lessons already in the more extensive course.
Regression metrics (R-squared MAE, MAE, MAPE)
ROC curve, as well as the region
Recall, Precision F1 score
Accuracy, balance, and accuracy
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