Logistic Regression model for binary classification tasks.
More...
#include <LogisticRegression.hpp>
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| | LogisticRegression (double learningRate=0.01, int iterations=1000, bool useBias=true) |
| | Constructor initializing the learning rate and iteration count. More...
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| void | train (const std::vector< std::vector< double >> &features, const std::vector< int > &labels) |
| | Train the model using features and labels. More...
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| int | predict (const std::vector< double > &features) const |
| | Predicts the class label for a given input. More...
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| double | predictProbability (const std::vector< double > &features) const |
| | Predicts the probability of class 1 for a given input. More...
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Logistic Regression model for binary classification tasks.
◆ LogisticRegression()
| LogisticRegression::LogisticRegression |
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double |
learningRate = 0.01, |
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int |
iterations = 1000, |
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bool |
useBias = true |
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) |
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inline |
Constructor initializing the learning rate and iteration count.
- Parameters
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| learningRate | The rate at which the model learns. |
| iterations | Number of training iterations. |
| useBias | Whether to include a bias term. |
◆ predict()
| int LogisticRegression::predict |
( |
const std::vector< double > & |
features | ) |
const |
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inline |
Predicts the class label for a given input.
- Parameters
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| features | Input feature vector. |
- Returns
- Predicted class label (0 or 1).
◆ predictProbability()
| double LogisticRegression::predictProbability |
( |
const std::vector< double > & |
features | ) |
const |
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inline |
Predicts the probability of class 1 for a given input.
- Parameters
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| features | Input feature vector. |
- Returns
- Probability of the input belonging to class 1.
◆ train()
| void LogisticRegression::train |
( |
const std::vector< std::vector< double >> & |
features, |
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const std::vector< int > & |
labels |
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) |
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inline |
Train the model using features and labels.
- Parameters
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| features | Input feature matrix. |
| labels | Binary labels (0 or 1). |
The documentation for this class was generated from the following file:
