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## Additional references
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### Useful posts
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[Logistic Regression](https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html)
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[A Gentle Introduction to Cross-Entropy](https://machinelearningmastery.com/cross-entropy-for-machine-learning/)
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[Loss functions](https://cs231n.github.io/neural-networks-2/#losses)
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[The cross-entropy cost function](http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function)
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[Cross-validation: evaluating estimator performance](https://scikit-learn.org/stable/modules/cross_validation.html?highlight=repeatedkfold)
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[L1 vs. L2 Loss function](http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/)
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[Entropy: How Decision Trees Make Decisions](https://towardsdatascience.com/entropy-how-decision-trees-make-decisions-2946b9c18c8)
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[ROC Curves and Precision-Recall Curves](https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/)
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[Information Gain and Mutual Information](https://machinelearningmastery.com/information-gain-and-mutual-information/#:~:text=Information%20gain%20is%20the%20reduction,before%20and%20after%20a%20transformation.)
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[Gradient Boosting & XGBoost](https://www.shirin-glander.de/2018/11/ml_basics_gbm/)
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- [Logistic Regression](https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html)
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- [A Gentle Introduction to Cross-Entropy](https://machinelearningmastery.com/cross-entropy-for-machine-learning/)
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- [Loss functions](https://cs231n.github.io/neural-networks-2/#losses)
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- [The cross-entropy cost function](http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function)
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- [Cross-validation: evaluating estimator performance](https://scikit-learn.org/stable/modules/cross_validation.html?highlight=repeatedkfold)
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- [L1 vs. L2 Loss function](http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/)
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- [Entropy: How Decision Trees Make Decisions](https://towardsdatascience.com/entropy-how-decision-trees-make-decisions-2946b9c18c8)
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- [ROC Curves and Precision-Recall Curves](https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/)
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- [Information Gain and Mutual Information](https://machinelearningmastery.com/information-gain-and-mutual-information/#:~:text=Information%20gain%20is%20the%20reduction,before%20and%20after%20a%20transformation.)
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- [Gradient Boosting & XGBoost](https://www.shirin-glander.de/2018/11/ml_basics_gbm/)
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[What’s considered a good Log Loss](https://medium.com/@fzammito/whats-considered-a-good-log-loss-in-machine-learning-a529d400632d)
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[On log loss](https://stats.stackexchange.com/questions/276067/whats-considered-a-good-log-loss/395774)
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[Decision Trees and Random Forests in Python](https://nickmccullum.com/python-machine-learning/decision-trees-random-forests-python/)
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- [On log loss](https://stats.stackexchange.com/questions/276067/whats-considered-a-good-log-loss/395774)
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- [Decision Trees and Random Forests in Python](https://nickmccullum.com/python-machine-learning/decision-trees-random-forests-python/)
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### Additional lecture notes:
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[Statistical Learning Theory -notes](https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/lecture-notes/MIT18_657F15_L2.pdf)
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[Logistic Regression -notes](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec09.pdf)
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[Decision Trees -notes](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec08.pdf)
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[Boosting -notes](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec10.pdf)
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[Convex optimization -notes](https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/lecture-notes/MIT18_657F15_L11.pdf)
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- [Statistical Learning Theory -notes](https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/lecture-notes/MIT18_657F15_L2.pdf)
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- [Logistic Regression -notes](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec09.pdf)
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- [Decision Trees -notes](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec08.pdf)
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- [Boosting -notes](https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec10.pdf)
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- [Convex optimization -notes](https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/lecture-notes/MIT18_657F15_L11.pdf)
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### Selected articles:
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[Classifying post-traumatic stress disorder](https://www.nature.com/articles/s41598-020-62713-5?sap-outbound-id=D4EE8DE8FA484F2A05F5264D4196D5BECD1CACD0&utm_source=hybris-campaign&utm_medium=email&utm_campaign=102_BHQ5340_0000009541_SREP_AWA_AW02_GL_EC_ML_HEALTHCARE&utm_content=EN_internal_20766_20210121&mkt-key=42010A0557EB1EDAA5CF8626FB94DC3E) |
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- [Classifying post-traumatic stress disorder](https://www.nature.com/articles/s41598-020-62713-5?sap-outbound-id=D4EE8DE8FA484F2A05F5264D4196D5BECD1CACD0&utm_source=hybris-campaign&utm_medium=email&utm_campaign=102_BHQ5340_0000009541_SREP_AWA_AW02_GL_EC_ML_HEALTHCARE&utm_content=EN_internal_20766_20210121&mkt-key=42010A0557EB1EDAA5CF8626FB94DC3E) |
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