Model selection in machine learning involves choosing the best model and hyperparameters to generalize to unseen data. This is done experimentally by splitting data into training and testing (and optionally validation) sets. Training occurs on the training set, and performance is evaluated on the test set to avoid overfitting. Hyperparameter tuning explores different parameter values, often using randomized search for efficiency. Cross-validation further improves robustness by averaging performance across multiple train-test splits. This segment explains the concept of a model's hypothesis space—the range of functions a machine learning model can represent—and how understanding this space is crucial for selecting a model appropriate for a given problem. It illustrates how input representation, output type, and the algorithm itself constrain the hypothesis space, using examples like single neuron models and neural networks with multiple nodes to highlight the differences in their capabilities and limitations. The segment emphasizes the importance of considering the hypothesis space before deciding on a model. This segment differentiates between model parameters (trained using data) and hyperparameters (tunable aspects not directly updated during training). It uses the example of single neuron models and neural networks to illustrate how parameters like weights and biases are adjusted during training, while hyperparameters like learning rate, number of iterations, and network architecture are manually set. The segment introduces the model selection problem—choosing the best model among different options—and its close relationship to hyperparameter tuning, emphasizing the need for a systematic approach to explore these options. This segment introduces the crucial concept of separating data into training and test sets for experimental validation of models. It explains how this approach allows for evaluating a model's ability to generalize to unseen data, which is the primary goal of machine learning. The segment uses the example of overfitting—where a model becomes too specific to the training data—to illustrate the importance of using a test set to assess generalization performance. It contrasts a model that perfectly fits the training data but generalizes poorly with a model that has some training error but better generalization.
