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Key examples include Linear Regression for predicting prices, Logistic Regression for classification tasks, and DecisionTrees for decision-making. Linear Regression predicts continuous outcomes, like housing prices. DecisionTrees visualize decision-making processes for better understanding.
Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression DecisionTrees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearestNeighbors Random Forest What do they mean? Often, these trees adhere to an elementary if/then structure.
Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression DecisionTrees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearestNeighbors Random Forest What do they mean? Often, these trees adhere to an elementary if/then structure.
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. Decisiontrees are easy to interpret but prone to overfitting.
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