The goal of modeling is to approximate real-life situations by identifying and encoding patterns in data. Models make mistakes if those patterns are overly simple or overly complex. In Part 1, we created a model that distinguishes homes in San Francisco from those in New York. Now, we’ll talk about tuning and the Bias-Variance tradeoff.
Subscribe to newsletter
Best design and frontend links delivered to your inbox every day and week. No spam, unsubscribe at anytime.