Verfasst von / Written by Sebastian F. Genter
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validation
- fundamentals
Validation is the process of evaluating a machine learning model during or after training) using a separate subset of data called the validation set. The purpose of validation is to assess how well the model generalizes to new, unseen data that it was not trained on. It helps in detecting overfitting, where the model performs well on the training set but poorly on the validation set, and in fine-tuning hyperparameters. Validation is a crucial step in the model development workflow, helping to select the best performing model and avoid overfitting before the final evaluation on the test set.
validation loss
- fundamentals
The loss calculated on the validation set. Validation loss is a key metric monitored during training) to assess the model's generalization ability on unseen data. If the training loss continues to decrease while the validation loss starts to increase, it is a strong indicator of overfitting. Monitoring validation loss helps in deciding when to stop training) (e.g., using early stopping) and in selecting the best model based on its performance on data it hasn't seen during training.
validation set
- fundamentals
A subset of a dataset used to evaluate a machine learning model during or after training) and to tune hyperparameters. The validation set is kept separate from the training set and is used to estimate the model's performance on unseen data. Unlike the test set, the validation set is used throughout the model development cycle to guide decisions about model architecture, hyperparameters, and early stopping without the risk of information leakage from the final test set.
value imputation
- fundamentals
A preprocessing technique used to handle missing values in a dataset. Value imputation involves filling in the missing values with substituted values. Common strategies include replacing missing values with the mean, median, or mode of the existing data for that feature, or using more sophisticated methods like k-nearest neighbor imputation or machine learning models to predict the missing values. The choice of imputation strategy can significantly impact the performance of the downstream model.
vanishing gradient problem
- fundamentals
A challenge encountered during the training) of deep neural networks, particularly recurrent neural networks and older feedforward networks that use sigmoid or tanh activation functions. The vanishing gradient problem occurs when the gradients of the loss function with respect to the early layers of the network become extremely small during backpropagation. This causes the parameter updates for these early layers to be very small, effectively preventing them from learning and hindering the network's ability to capture long-term dependencies in the data. Modern architectures and techniques like ReLU activation functions, LSTMs, GRUs, and Transformers help to mitigate this problem.
variable importances
- Metric
A set of metrics that quantify the relative contribution or significance of each input feature to a model's predictions. Variable importances help in understanding which features are most influential in the model's decision-making process. Different models and algorithms may use different methods for calculating feature importance, such as examining the magnitude of learned weights (in linear models), measuring the reduction in impurity (in decision trees and random forests), or using techniques like permutation variable importances. Understanding variable importances can aid in feature selection, model interpretation, and debugging.
variational autoencoder (VAE)
A type of generative neural network architecture. A variational autoencoder (VAE) consists of an encoder and a decoder. The encoder maps the input data to a probabilistic latent space by learning the parameters (mean and variance) of a probability distribution for each input example. The decoder then samples from this latent space and attempts to reconstruct the original input data. VAEs are trained to minimize a loss function that balances the accuracy of the reconstruction with the similarity of the learned latent space distribution to a prior distribution (typically a normal distribution). VAEs can be used for generating new data samples by sampling from the learned latent space.
vector
- TensorFlow
In mathematics and machine learning, a vector is a one-dimensional array of numbers. It can be thought of as a list of scalar values. Vectors are fundamental data structures used to represent data points, feature vectors, model parameters (like biases), and embedding vectors. A vector has a rank of 1 and its shape is defined by its length (e.g., a vector with 5 elements has a shape of (5,)).