Loss decay is a technique used in machine learning where the influence of the loss function is gradually reduced during training. This helps the model make larger adjustments in the beginning and smaller, more precise tweaks as it improves. The approach can help prevent overfitting and guide the training process to a more stable final…
Label calibration is the process of adjusting the confidence scores produced by a machine learning model so they better reflect the true likelihood of an outcome. This helps ensure that, for example, if a model predicts something with 80 percent confidence, it will be correct about 80 percent of the time. Calibrating labels can improve…
Feature ranking is the process of ordering the input variables of a dataset by their importance or relevance to a specific outcome or prediction. It helps identify which features have the most influence on the results of a model, allowing data scientists to focus on the most significant factors. This technique can make models simpler,…
Hyperparameter tweaks refer to the process of adjusting the settings that control how a machine learning model learns from data. These settings, called hyperparameters, are not learned by the model itself but are set by the person training the model. Changing these values can significantly affect how well the model performs on a given task.
Weight freezing is a technique used in training neural networks where certain layers or parameters are kept unchanged during further training. This means that the values of these weights are not updated by the learning process. It is often used when reusing parts of a pre-trained model, helping to preserve learned features while allowing new…
Overfitting checks are methods used to ensure that a machine learning model is not just memorising the training data but can also make accurate predictions on new, unseen data. Overfitting happens when a model learns too much detail or noise from the training set, which reduces its ability to generalise. By performing checks, developers can…
Fine-tune sets are collections of data specifically chosen to train or adjust an existing artificial intelligence model, making it perform better on a certain task or with a particular type of input. These sets usually contain examples and correct answers, helping the AI learn more relevant patterns and responses. Fine-tuning allows a general model to…
Data sampling is the process of selecting a smaller group from a larger set of data to analyse or make predictions. This helps save time and resources because it is often not practical to work with every single piece of data. By carefully choosing a representative sample, you can still gain useful insights about the…
Drift scores are numerical values that measure how much data has changed over time compared to a previous dataset. They help identify shifts or changes in the patterns, distributions, or characteristics of data. These scores are often used to monitor whether data used by a machine learning model is still similar to the data it…
Performance metrics are measurements used to assess how well a system, process, or individual is working. They help track progress, identify strengths and weaknesses, and guide improvements. Good metrics are clear, relevant, and easy to understand so that everyone involved can use them to make better decisions.