IST 664 Week 3 Key Terms

Where each word in a sentence is assigned a grammatical category such as noun, verb, adjective or adverb. This process help machines to understand the structure and meaning of sentences by identifying the roles of words and their relationships.

A word can have multiple meanings in the sae speech part
A word may function as multiple parts of speech

Human interaction is required during the training phase to accurately label data. Once the model has learned the relationship between the input and output data, it classifies new and unseen datasets and predicts outcomes.

The training of models is done on raw and unlabeled training data, without the need for human intervention. It is used to identify patterns and trends in raw datasets, or to cluster similar data into a specific number of groups

Helps us to make decisions by mapping out different choices and their possible outcomes. It’s used in machine learning for tasks like classification and prediction

A table that summarizes how successful a classification model is

x-axis: the label that the model predicts:
y-axis: the actual label
Diagonal elements: the predicted label is equal to the true label
Off-diagonal elements: mislabeled by the classier.

The higher the diagonal values of the confusion matrix the better

Cause machine learning models to become biased towards the majority class, leading to poor predictive performance on the critical minority class. This often results in misleadingly high accuracy, as the model ignores the underrepresented data

Data is split 80/20 into a training and validation set. Used to estimate the skill of machine learning models on unseen data, preventing overfitting by ensuring every data point is used for both training and validation