Chapter 7: Supervised Learning – Logistic Regression
Logistic Regression is a machine learning algorithm designed for binary classification problems, transforming categorical outcomes into probabilities using the sigmoid function. It distinguishes between regression and classification methods, showcases dataset preparation and model training, and evaluates models' performance through accuracy scores and confusion matrices.
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What we have learnt
- Logistic Regression is primarily used for binary classification tasks.
- The sigmoid function plays a crucial role in converting the model outputs to probabilities.
- Evaluating the model is essential with metrics like accuracy and confusion matrix.
Key Concepts
- -- Logistic Regression
- A supervised machine learning algorithm used for binary classification problems.
- -- Sigmoid Function
- A mathematical function that converts predicted values into probabilities between 0 and 1.
- -- Confusion Matrix
- A table used to evaluate the performance of a classification model, detailing true positives, true negatives, false positives, and false negatives.
- -- Accuracy
- The ratio of correctly predicted instances to the total instances, used as a measure of model performance.
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