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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. K-Means ClusteringK-means clustering partitions data into k distinct clusters based on feature similarity.
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).
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.
Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. Predictiveanalytics uses historical data to forecast future trends, such as stock market movements or customer churn.
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