Interaction features are derived features in machine learning models that capture the combined effect of two or more variables, often leading to improved prediction accuracy by revealing complex relationships. They are particularly useful in models where the relationship between variables is not purely additive, such as in non-linear or high-dimensional data contexts.

Interaction Features

Interaction features

derived features

Machine Learning Models are algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed to perform the task. They are foundational to artificial intelligence, enabling systems to improve their performance on a specific task with experience.

machine learning models

combined effect

Prediction accuracy measures how often a predictive model correctly forecasts outcomes, serving as a fundamental metric for evaluating model performance. High accuracy indicates reliable predictions, but it must be considered alongside other metrics like precision and recall to ensure a comprehensive assessment of the model's effectiveness.

prediction accuracy

complex relationships

Non-linear data

High-dimensional data refers to datasets with a large number of variables or features, often leading to challenges in analysis due to the 'curse of dimensionality'. This complexity can result in overfitting, increased computational cost, and difficulties in visualization and interpretation, necessitating advanced techniques for effective analysis.

Relevant Degrees

Your Lessons