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|Model = GPT-4

...umber can vary based on both machine memory capacity and the needs of each model and dataset. ...el processes 50 examples per iteration. If the batch size is 200, then the model processes 200 examples per iteration.

Evaluation of a model's performance in machine learning is essential to determine its capacity fo ...ces while recall is its capacity for recognizing all positive instances. A model with high precision typically makes few false positives while one with high

...nd affect machine learning models, including through biased training data, model assumptions, and evaluation metrics.

...ses or predicts a continuous output value. When using a linear kernel, the model assumes a linear relationship between the input features and the output. * '''Independence of Errors''' - The errors (residuals) in the model are assumed to be independent of each other. This means that the error at o

...uilding blocks. Each block can be seen as a layer in your machine learning model. ...any blocks makes it stronger, having multiple layers in a machine learning model enhances its capacity for understanding and making decisions.

[[Model]] will train on the Z-score instead of raw values

...ned by the [[hyperparameter]] [[batch size]]. If the batch size is 50, the model processes 50 examples before updating it's parameters - that is one iterati ...data|training]] [[dataset]]. By repeating this process multiple times, the model learns from its errors and improves its [[accuracy]].

|Model = GPT-4

...representation that illustrates the performance of a binary classification model. The curve is used to assess the trade-off between two important evaluation ...positive predictions made by the model. High precision indicates that the model is making fewer false positive predictions. Precision is defined as:

A '''multimodal model''' in [[machine learning]] is an advanced computational approach that invol ...o handle and process multiple data modalities simultaneously, allowing the model to learn richer and more comprehensive representations of the underlying da

|Model = GPT-4

...ive class. The classification threshold is set by a person, and not by the model during [[training]]. A logistic regression model produces a raw value of between 0 to 1. Then:

...y divide a [[dataset]] into smaller [[batch]]es during [[training]]. The [[model]] only trains on these mini-batches during each [[iteration]] instead of th ...nal machine learning relies on [[batch]] [[gradient descent]] to train the model on all data in one iteration. Unfortunately, when the dataset grows large,

* [[Model training]]: Code and configuration files for training and evaluating machin * [[Model deployment]]: Scripts and configuration files for deploying trained models

...e learning model contains unequal representation or historical biases, the model is likely to perpetuate these biases in its predictions and decision-making ...eature selection''': The choice of features (or variables) to include in a model can inadvertently introduce in-group bias if certain features correlate mor

...ger PR AUC indicates better classifier performance, as it implies that the model has both high precision and high recall. The maximum possible PR AUC value

...t the learning algorithm itself, incorporating fairness constraints during model training. Some examples include adversarial training and incorporating fair * '''Post-processing techniques''': After a model has been trained, post-processing techniques adjust the predictions or deci

...machine learning model's predictions. These metrics aim to ensure that the model's outcomes do not discriminate against specific subpopulations or exhibit u ...optimizing for one metric can inadvertently worsen the performance of the model with respect to another metric.

...g since they do not take into account the class imbalance. For instance, a model that always predicts the majority class may have high accuracy on an unbala ...t for the imbalance; threshold moving alters the decision threshold of the model in order to increase sensitivity towards minority classes; and ensemble met