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K nearest neighbors algorithm In Machine Learning
- K-nearest neighbors (KNN) is a supervised machine learning algorithm suitable for classification and regression tasks by finding the k-nearest data points to predict the category or value of a new data point.
- Pros of KNN include easy implementation, understanding, and versatility for both regression and classification tasks.
- Cons of KNN are its computational expense for large datasets and sensitivity to the hyperparameter k.
- The choice of k in KNN affects bias and computational cost, with lower k values leading to more bias and higher k values being more expensive.
- KNN relies on calculating distances between data points using methods like Euclidean, Manhattan, and Minkowski.
- Real-world applications of KNN include image classification, fraud detection, recommender systems, house price prediction, and customer grouping by interests.
- In a given dataset example, the KNN algorithm is applied to predict breast cancer diagnosis based on various features like radius, texture, area, etc.
- The process involves preprocessing data, handling outliers, encoding non-numeric data, training the model, testing its accuracy, and tuning the hyperparameter k.
- A program is provided for implementing KNN in Python using scikit-learn, featuring data preprocessing, modeling, outlier detection, and basic analysis.
- An example is shown where the model's accuracy is evaluated for different values of k to demonstrate the impact of choosing the neighbor count on training and testing scores.
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