Classification Algorithms
Classification techniques are essential for predicting labels or categories within datasets, utilizing algorithms such as Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). These methods are critically evaluated using metrics like accuracy, precision, recall, and F1-score, alongside the confusion matrix to visualize prediction results. Proper selection of classifiers is vital based on the complexity of the problem, the data size, and the interpretability of the results.
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What we have learnt
- Classification is a supervised learning technique used to categorize data into discrete classes.
- Common classification algorithms include Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN).
- Model evaluation techniques include using a confusion matrix and understanding classification metrics such as accuracy, precision, recall, and F1-score.
Key Concepts
- -- Classification
- A supervised learning technique aimed at predicting a category or label for new observations based on past data.
- -- Logistic Regression
- A statistical method used for binary classification that predicts the probability of a certain class based on one or more predictor variables.
- -- Decision Tree
- A model that uses a tree-like graph of decisions based on feature splits to predict an outcome.
- -- KNearest Neighbors (KNN)
- An algorithm that classifies instances based on the classes of their nearest neighbors in the feature space.
- -- Confusion Matrix
- A table used to evaluate the performance of a classification model by comparing predicted values to actual values.
- -- Precision
- The ratio of correctly predicted positive observations to the total predicted positives.
- -- Recall
- Also known as sensitivity or true positive rate, it is the ratio of correctly predicted positive observations to all actual positives.
- -- F1Score
- The harmonic mean of precision and recall, providing a balance between the two metrics.
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