From Prediction to Decisions: Why ML Needs a Threshold
Data Science: The Hard Parts by Daniel Vaughan (ISBN 978-1-098-14647-4)
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Classification
Data Science Foundations Chapter 10 Part 2: Time Series, Classification, and Clustering Models
This is Part 2 of 2 for Chapter 10. In Part 1 we covered how to pick the right model and looked at regression. Now we get into the rest: time series, classification, clustering, and association analysis.
Finding the Line: Predicting Stocks With Support Vector Machines
If you’re looking for the heavyweight champion of classification models, you’ve probably heard of Support Vector Machines (SVM). In Chapter 12 of Data Analytics for Finance Using Python, we see why this model is a favorite for high-precision tasks.
The Branching Path: Investment Management With Decision Trees
We’ve talked about random forests, but sometimes it’s better to look at the individual trees. In Chapter 6 of Data Analytics for Finance Using Python, we dive into the Decision Tree Classifier.
Probabilistic Profits: Stock Decisions With Gaussian Naive Bayes
If you’re looking for a machine learning model that’s fast, efficient, and actually outshines more complex models in some cases, you need to look at Gaussian Naive Bayes (GNB).
Buy or Sell? Stock Investment Strategy With Logistic Regression
In Chapter 3 of Data Analytics for Finance Using Python, we move from predicting exact prices to making a much simpler, but arguably more important, decision: Should I buy or sell?