Cross Validation is often used as a tool for model selection across classifiers. As discussed in detail in the following paper https://ssrn.com/abstract However, one question often pops up: how to choose K in K-fold cross validation. The rule-of-thumb choice often suggested by literature based on non-financial market is K 10. The question is: is it true for Financial Market?
As the name of the suggests, cross-validation is the next fun thing after learning Linear Regression because it helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Everything is explained below with Code. We are copying the target in dataset to y variable. To see the dataset uncomment the print line.
The k-fold cross-validation procedure is a standard method for estimating the performance of a machine learning algorithm or configuration on a dataset. A single run of the k-fold cross-validation procedure may result in a noisy estimate of model performance. Different splits of the data may result in very different results. Repeated k-fold cross-validation provides a way to improve the estimated performance of a machine learning model. This involves simply repeating the cross-validation procedure multiple times and reporting the mean result across all folds from all runs.
In this post, I'll give a background on choosing an algorithm, then using a validation technique. For the technique, I'll show how to apply it, and how it can be built using the Talend Studio without hand coding. Given a prediction scenario involving a machine learning algorithm, the first question to ask is what is the appropriate machine learning algorithm? Taking the example of predicting a user's activity based on mobile phone accelerometer data, we must be able to classify a category for the data (resting, walking, or running). As Talend leverages Spark MLlib out-of-the-box, we evaluate some of the popular algorithms which fall under classification.
You need to know how well your algorithms perform on unseen data. The best way to evaluate the performance of an algorithm would be to make predictions for new data to which you already know the answers. The second best way is to use clever techniques from statistics called resampling methods that allow you to make accurate estimates for how well your algorithm will perform on new data. In this post you will discover how you can estimate the accuracy of your machine learning algorithms using resampling methods in Python and scikit-learn. Evaluate the Performance of Machine Learning Algorithms in Python using Resampling Photo by Doug Waldron, some rights reserved.