Machine Learning Terms

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The following claims were verified through Perplexity Sonar before the questions were finalised. The host can reference any of them during play:

• Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed[6].
• In supervised learning, models are trained on labeled data to classify data or predict outcomes[3][6].
• Unsupervised learning uses patterns from unlabeled datasets to discover hidden relationships or groupings like clustering[3][9].
• Reinforcement learning trains agents to make sequential decisions by maximizing cumulative rewards through trial and error interactions[3][10].
• A feature is the input variable or independent variable used by a model to learn relationships and make predictions[3][7].
• The label is the output variable or target value that a machine learning model aims to predict[3][7].
• An algorithm is a set of rules or instructions that defines the learning process and allows models to make predictions from data[2][5].
• Accuracy is the percentage of correct predictions made by a machine learning model[4].
• Convergence is the state reached during training when the model's changes become very small between each iteration[4].
• Feature engineering is the process of transforming raw data into meaningful input features to improve model performance[3][10].
• Underfitting occurs when a model is too simple to capture the underlying structure of the data, leading to poor predictions[10].
• Inference is the phase where a trained model is used to predict values using live data or put into production[3].