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Performance Evaluation Of DataMining Models

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0:00 Introduction
1:01 What Are we Predicting?
1:50 The Big Insight
2:31 The Naive Benchmark
3:24 Prediction Accuracy Metrics
4:25 Training vs Validation
5:18 Lifts & Charts
5:45 Cumulative Gains Chart
6:34 Decile Lift Chart
7:35 Classification Metrics
7:54 The Naive Rule
8:47 The Confusion Matrix
9:59 The Cutoff Decision
11:00 Sensitivity vs. Specificity
12:05 The ROC Curve
13:09 Cost-Sensitive Learning
13:32 Not All Mistakes Are Equal
14:25 Average Misclassification Cost
15:26 Oversampling for Rare Events
16:26 Key Takeaways
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Nair, A. 2025. Structured Thinking for Smarter Decisions. How 100+ Proven Frameworks Help You See Patterns, Gain Clarity, and Act with Confidence. https://www.amazon.com/dp/B0FNCXPT1C

Recommended books for learning more about Multiple Linear Regression, Data Mining & Deep Learning:

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. "
O'Reilly Media, Inc.". https://amzn.to/3ZxF2wX
Goodfellow, I., Bengio, Y. & Courville, A. (2017). Deep Learning (Adaptive Computation and
Machine Learning Series). Cambridge, MA, USA: MIT press. https://amzn.to/4k49AP9
Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan Kaufmann.
https://amzn.to/4jjoy2P
Kazil, J., & Jarmul, K. (2016). Data wrangling with python: tips and tools to make your life easier. "O'Reilly Media, Inc.". https://amzn.to/3SNp5Pr
Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for
business analytics: concepts, techniques, and applications in Python. John Wiley & Sons. https://amzn.to/3SP8YAW
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