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k-NearestNeighbors (k-NN) k-NN is a simple algorithm that classifies new instances based on the majority class among its knearest neighbours in the training dataset. Example: Recommending movies to users based on ratings given by similar users in a collaborative filtering system.
Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearestNeighbors Random Forest What do they mean? AI models can be trained to recognize patterns and make predictions.
Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearestNeighbors Random Forest What do they mean? AI models can be trained to recognize patterns and make predictions.
Supervised learning is commonly used for risk assessment, image recognition, predictiveanalytics and fraud detection, and comprises several types of algorithms. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g., temperature, salary).
Underfitting happens when a model is too simplistic and fails to capture the underlying patterns in the data, leading to poor predictions. Predictiveanalytics uses historical data to forecast future trends, such as stock market movements or customer churn. Some algorithms work better with small datasets (e.g., Random Forests).
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