Lesson 16 — Machine Learning: Cross-Validation and Model Selection
Cross-validation and model selection are important techniques for assessing and improving the performance of machine learning models. In this lesson, we’ll discuss these techniques with a focus on building intuition.
Cross-Validation
Cross-validation is a technique for estimating the performance of a machine learning model on unseen data. It involves dividing the dataset into multiple smaller sets and using different combinations of these sets for training and validation. This process helps to ensure that the model is evaluated on different parts of the data, providing a more reliable estimate of its performance.
Intuition: Imagine you’re a teacher grading a student’s performance. If you base the grade on just one exam, it may not accurately reflect their overall understanding. But if you evaluate their performance across multiple exams, you’ll get a more accurate measure of their knowledge.
There are different types of cross-validation:
K-Fold Cross-Validation: The dataset is divided into K equal-sized folds. The model is trained on K-1 folds and validated on the remaining fold. This process is repeated K times, with each fold used as the validation set once. The final performance is the average of the performance across all K iterations.
Leave-One-Out Cross-Validation: A special case of K-Fold Cross-Validation where K equals the number of data points. Each data point is used as a validation set exactly once, with the model trained on the remaining data points. This method can be computationally expensive for large datasets.
Model Selection
Model selection is the process of choosing the best machine learning model for a given problem. This involves comparing different models or variations of models, using cross-validation to estimate their performance on unseen data.
Intuition: Think of model selection as a competition where you’re the judge, and different models are the contestants. You want to find the best performer based on their ability to generalize to new data, so you use cross-validation as a reliable way to measure their performance.
There are several techniques for model selection:
Grid Search: An exhaustive search through a predefined set of hyperparameters, evaluating each combination using cross-validation. The best set of hyperparameters is chosen based on the highest cross-validated performance.
Random Search: Instead of searching through all possible combinations of hyperparameters like in grid search, random search samples random combinations of hyperparameters within a predefined range, evaluating each using cross-validation. This method is less computationally expensive than grid search but may not find the optimal combination.
Bayesian Optimization: An intelligent search method that uses probabilistic models to guide the search for optimal hyperparameters, based on prior evaluations. This method is more efficient than grid and random search and often finds better hyperparameter combinations in fewer iterations.
In summary, cross-validation is a crucial technique for estimating the performance of machine learning models on unseen data, while model selection helps us find the best model or set of hyperparameters for a given problem. Various cross-validation methods and model selection techniques can be used to achieve these goals and improve the overall performance of our models.
