Making Predictive Models Robust: Holdout vs Cross-Validation

[unable to retrieve full-text content]The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.
Original Post: Making Predictive Models Robust: Holdout vs Cross-Validation

Understanding the Bias-Variance Tradeoff: An Overview

Previous post            Tweet Tags: Bias, Cross-validation, Model Performance, Variance A model’s ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results. By Matthew Mayo, KDnuggets. A few years ago, Scott Fortmann-Roe wrote a great…
Original Post: Understanding the Bias-Variance Tradeoff: An Overview

How to Compute the Statistical Significance of Two Classifiers Performance Difference

Previous post Next post            Tweet Tags: Classifier, Cross-validation, Model Performance To determine whether a result is statistically significant, a researcher would have to calculate a p-value, which is the probability of observing an effect given that the null hypothesis is true. Here we are demonstrating how you can compute difference between two models using it. By Theophano…
Original Post: How to Compute the Statistical Significance of Two Classifiers Performance Difference