data science · machine learning

Interpretability of ML

https://github.com/jphall663/awesome-machine-learning-interpretability https://christophm.github.io/interpretable-ml-book/index.html Global Interpretability Partial Dependence and Partial Dependence Plot (PDP) Individual Conditional Expectation (ICE) Total and two-way H Statistics Global Feature importance using permutation Global surrogate model Local Interpretability Local Interpretable Model-agnostic Explanations (LIME) Shapley additive explanation An intuitive way to understand the Shapley value is the following illustration: The feature values enter a… Continue reading Interpretability of ML

machine learning

Deep Learning

http://www.cs.toronto.edu/~bonner/courses/2014s/csc321/lectures/lec5.pdf DNN: gradient check, learning rate, initialization(not zero, asymmetric), local minimum, plateau momentenm  (exponential average of previous gradients, pointing to the same direction), saddle point() Tips for training Regularization: norm penalties, early stopping, data augmentation, drop out (hidden units cannot co-adapt, generally used, test time:expectation, geometric average for signel layer) Auto encoder: sparse AE, denoising… Continue reading Deep Learning

machine learning

Kinds of Machine Learning

Kinds of learning: Based on the information available: Supervised learning, Reinforcement learning, Unsupervised learning Based on the role of the learner: Passive learning, Active learning Scenarios: Membership Query Synthesis: the learner may request labels for any unlabeled instance in the input space Stream-Based Selective Sampling: it can first be sampled from the actual distribution, and then… Continue reading Kinds of Machine Learning