ROC curves and AUC values are common evaluation metric for binary classification models. Although there are some criticism of it especially its’s appropritatenes in evaluating models built with imbalanced data, they still remain the most popular evaluation metric for binary classification models. In the case of highly imbalanced classification, the...
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Machine Learning Interpretability With R
Introduction The two main packages in R for machine learning interpretability is the iml and DALEX. H2o package also has built in functions to perform some interpretability such as partial dependence plots. DALEX and iml are model agnostic as such can be used to explain several supervised machine learning models...
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Machine Learning Interpretability
Permutation Importance, Partial Dependence Plots, SHAP values, LIME, lightgbm,Variable Importance
Introduction Machine learning algorithms are often said to be black-box models in that there is not a good idea of how the model is arriving at predictions.This has often hindered adopting machine learning models in certain industires where interpretation is key. Examples of such areas include financial institutions who are...
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Distributed Machine Learning with Spark ML.
Elastic Net Logistic Regression, Gradient-Bossting Machines, Random Forest Models in Spark
Distributed Machine Learning with Spark ML
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Hyperparameter Tuning The Alternating Least-Squares Algorithm for A Recommender System.
Personalized Recommendation with Matrix Factorization
Colaborative Filtering : Hyperparameter Tuning Alternating Least Squares Algorithm
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