Rapid Fire on Machine Learning

Thinking about key business metrics, often shortened as KPI’s (Key Performance Indicators), is an essential part of a data scientist’s job. Here are a few examples, but you should practice brainstorming your own. Tip: When in doubt, start with the easier question of “how does this business make money?” S-a-a-S startup: Customer lifetime value, new … Read more

Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling. Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models. Bagging is performed in parallel. In the context of machine learning, bagging, short … Read more

They average out biases, reduce variance, and are less likely to overfit. There’s a common line in machine learning which is: “ensemble and get 2%.” This implies that you can build your models as usual and typically expect a small performance boost from ensembling. Ensemble methods are often superior to individual models due to several … Read more

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs. False Positive Rate (x- axis). AUC is area under the ROC curve, and it’s a common performance metric for evaluating binary classification models. It’s equivalent to the expected probability that a uniformly drawn random positive is ranked before … Read more

Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying documents by subject matter. LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words. The “Dirichlet” distribution is simply a distribution of distributions. In LDA, documents are distributions of … Read more