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#SupervisedLearning is a type of #MachineLearning where an algorithm is trained on a labeled dataset to predict outcomes for new data
The labeled dataset contains input variables and the desired output, and the algorithm uses this information to make predictions
The labeled dataset contains input variables and the desired output, and the algorithm uses this information to make predictions
In #SupervisedLearning, the algorithm is constantly adjusting its parameters to minimize the prediction error
One of the most popular algorithms for #SupervisedLearning is #LinearRegression, used for prediction problems where the target is continuous
One of the most popular algorithms for #SupervisedLearning is #LinearRegression, used for prediction problems where the target is continuous
Another common algorithm is #LogisticRegression, used for classification problems where the target is binary
#DecisionTrees and #RandomForests are commonly used for both regression and classification problems
#DecisionTrees and #RandomForests are commonly used for both regression and classification problems
Decision trees based Machine Learning models are some of the best performant algorithms in eras of predictive capability, especially on small and heterogenous datasets.
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They also provide an unparalleled level of interpretability compared to all other non-linear algorithms. However, they are very hard to optimize on Von Neumann architecture machines due to their non-uniform memory access patterns.
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In groundbreaking work published in Nature Communications a team of researchers has shown that analog content addressable memory (CAM) devices with in-memory calculation can dramatically accelerate tree-based model inference, as much as 10**3 over the conventional approaches 3/4
