Linear Regression
- Predicts numerical outcomes using a straight-line relationship.
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Learning Rate
- Controls how much weights change during training.
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Target
- The outcome or label the model learns to estimate.
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Logistic Regression
- Used for binary outcomes like yes/no or true/false.
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Model
- A program that maps inputs to outputs based on data patterns.
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Support Vector Machine
- Separates classes using the widest possible margin.
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Training
- The process of adjusting model parameters to learn from data.
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Naive Bayes
- Uses statistics to predict categories based on likelihood.
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Testing
- Uses unseen data to see how well the model learned.
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