10.7: Key Terms
- Page ID
- 58333
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Key Terms and Definitions
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Supervised Learning — ML where the model is trained on labeled data with known target outcomes.
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Unsupervised Learning — ML where the model finds hidden patterns in data without labeled outcomes.
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Linear Regression — Algorithm predicting a continuous numeric output by fitting a line/hyperplane to data.
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Logistic Regression — Classification algorithm modeling the probability of a binary outcome using a sigmoid function.
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Decision Tree — Algorithm that splits data through a hierarchy of if-then rules to make predictions.
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Random Forest — An ensemble of many decision trees whose predictions are averaged/voted for better accuracy.
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K-Means Clustering — Unsupervised algorithm that groups data into K clusters based on distance to cluster centers.
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Overfitting — When a model learns noise in training data and performs poorly on new data.
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Underfitting — When a model is too simple to capture the underlying patterns in the data.
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Train/Test Split — Dividing data into separate sets for model training and evaluation to assess generalization.
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Confusion Matrix — A table showing true positives, false positives, true negatives, and false negatives for a classifier.
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Precision — Of all predicted positives, the proportion that are actually positive (TP / (TP+FP)).
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Recall — Of all actual positives, the proportion correctly identified (TP / (TP+FN)).
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F1 Score — The harmonic mean of precision and recall, balancing both metrics.
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Feature — An input variable (attribute or column) used by an ML model to make predictions.