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Areas Served: ![Data Science Evaluation Metrics — Unravel Algorithms for Classification [Part 1]](https://i3.wp.com/miro.medium.com/v2/resize:fit:6013/0*pnQa_5B3SdT1Ov2o?w=1920&resize=1920,1280&ssl=1 "Data Science Evaluation Metrics — Unravel Algorithms for Classification [Part 1]")
Data Science Evaluation Metrics — Unravel Algorithms for Classification [Part 1]
Author(s): Maximilian Stäbler Originally published on Towards AI. Photo by Luke Chesser on Unsplash Data Science, Machine Learning Lessons learned about evaluation metrics for classification tasks. If you do not know how to ask the right question, you discover nothing.– W. Edwards …
Top 10 Interview Questions on Evaluation Metrics in Machine Learning
Author(s): Simranjeet Singh Originally published on Towards AI. Introduction Evaluation metrics are quantitative measures used to assess the performance of machine learning models. They are important because they provide a systematic and objective way of comparing different models and measuring their success …
Deep Dive Into Confusion Matrix
Author(s): Saurabh Saxena Originally published on Towards AI. Model Evaluation Precision (TPR), Recall (PPV), TNR, FPR, FNR, NPV, F1 Score, Accuracy, Balanced Accuracy, LR+, LR- Image by Author In the field of Data Science, model evaluation is the key component of the …
Introduction to Confusion Matrix
Author(s): Saurabh Saxena Originally published on Towards AI. Model Evaluation What is Confusion Matrix and how to plot it in Python? Image by Author The Confusion Matrix is the visual representation of the Actual VS Predicted values. It is a performance evaluation …
Improve Your Classification Models With Threshold Tuning
Author(s): Edoardo Bianchi Originally published on Towards AI. A practical and essential guide This member-only story is on us. Upgrade to access all of Medium. Photo by Denisse Leon on Unsplash Threshold tuning is an important and necessary step in the Data …
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