LESSON 8 - Supervised learning 3 Flashcards
What is the definition of generalization in the context of learning?
Generalization is the ability to apply acquired knowledge to new examples of a problem, extending learned skills and information to unseen instances.
What conditions are necessary for achieving generalization in machine learning?
Two necessary conditions for generalization are having input variables related to the target and ensuring a sufficiently large training set that represents diverse examples.
Why is it crucial for input variables to contain information related to the target in the context of generalization?
Input variables must contain relevant information to establish a function linking input to output, facilitating the generalization process. Unrelated information hinders accurate predictions.
How does the distinction between interpolation and extrapolation relate to the challenges of machine learning?
Interpolation, estimating values within known data points, is usually possible in machine learning. However, extrapolation, inferring values outside the trained range, is challenging and may result in poor generalization.
What is overfitting, and how does it impact machine learning models?
Overfitting occurs when a machine learning model memorizes training data, leading to poor generalization. While it performs well on training examples, it struggles with new instances.
What is an example of overfitting in a multilayer network, and why does it happen?
Overfitting in a multilayer network is observed when the network diverges significantly from trained examples as it attempts to extrapolate beyond its experience, especially in non-linear regression problems.
How does overfitting relate to the concept of interpolation and extrapolation in machine learning?
Overfitting tends to occur when a model excessively fits the training data, leading to excellent interpolation within the training set but poor extrapolation beyond it.
What are some common reasons overfitting occurs in machine learning?
Overfitting is often a result of irregular relationships between input and output, the presence of many exceptions, and the influence of noisy data that the model learns, affecting its ability to generalize.
How can the complexity of neural networks be controlled to avoid overfitting?
Neural network complexity can be controlled by limiting the number of hidden neurons. In cases where a linear solution suffices, avoiding an excessively large number of hidden neurons prevents overfitting.
What is the significance of early stopping in machine learning, particularly in neural networks?
Early stopping is crucial in preventing overfitting by interrupting the learning phase when the network starts overtraining, ensuring a balance between learning and avoiding excessive complexity.
How is weight decay utilized to prevent overfitting in neural networks?
Weight decay, a regularization technique, reduces the complexity of a neural network by discouraging the growth of weak weights that fit noise in the data, aiding in avoiding overfitting.
What is the purpose of a test set in machine learning, and how is it different from a training set?
A test set, independent of the training set, assesses the performance of the machine learning model. It contains examples not used during training, providing a reliable measure of the model’s generalization.
Why is a validation set necessary in addition to a test set in machine learning?
The validation set helps optimize learning parameters, ensuring the model’s generalization performance. It guides decisions such as when to stop learning and prevents bias in parameter optimization.
What challenges arise when using the validation set as a training set, and why should they be avoided?
Using the validation set as a training set introduces bias, optimizing performance from the beginning. To avoid biased optimization, the validation set must remain independent and serve only to fine-tune parameters.
How is cross-validation employed to maximize training data in machine learning?
Cross-validation involves splitting data into multiple parts, training the system on most folds, and testing on the remaining one across iterations. It maximizes the use of training data for testing and parameter tuning.
