Machine Learning Glossary: ML Fundamentals

This page contains ML Fundamentals glossary terms. The number of correct classification predictions divided by the total number of predictions. Binary classification provides specific names for the different categories of correct predictions and incorrect predictions. Compare and contrast accuracy with precision and recall. Although a valuable metric for some situations, accuracy is highly misleading for others. Notably, accuracy is usually a poor metric for evaluating classification models that process class-imbalanced datasets. For example, suppose snow falls only 25 days per century in a certain subtropical city. Since days without snow (the negative class) vastly outnumber days with snow (the positive class), the snow dataset for this city is class-imbalanced. Imagine a binary classification model that is supposed to predict either snow or no snow each day but simply predicts "no snow" every day. This model is highly accurate but has no predictive power. Although 99.93% accuracy seems like very a impressive percentage, the model actually has no predictive power. Precision and recall are usually more useful metrics than accuracy for evaluating models trained on class-imbalanced datasets. A function that enables neural networks to learn nonlinear (complex) relationships between features and the label. The plots of activation functions are never single straight lines. In a neural network, activation functions manipulate the weighted sum of all the inputs to a neuron. To calculate a weighted sum, the neuron adds up the products of the relevant values and weights. A non-human program or model that can solve sophisticated tasks. For example, a program or model that translates text or a program or model that identifies diseases from radiologic images both exhibit artificial intelligence. Formally, machine learning is a sub-field of artificial intelligence. However, in recent years, some organizations have begun using the terms artificial intelligence and machine learning interchangeably. A number between 0.0 and 1.0 representing a binary classification model's ability to separate positive classes from negative classes. The closer the AUC is to 1.0, the better the model's ability to separate classes from each other. For example, the following illustration shows a classifier model that separates positive classes (green ovals) from negative classes (purple rectangles) perfectly. This unrealistically perfect model has an AUC of 1.0: Conversely, the following illustration shows the results for a classifier model that generated random results.