In this code a Random Forest Classifier using Stochastic Gradient Descent Optimizer is proposed. This code provides high and accurate results using Stochastic Gradient Decent Optimizer. The method of random forest (RF) is a generalized advanced decision trees methods or techniques in which the clinical data space recursively portioned (usually binary split) according to the values of one or more predictor variables, such that the observations within a portion becomes more and more homogeneous. Moreover, the RF techniques come with a built-in protection against the over fitting by using a part of the data sets that each tree in the forest has not been calculated by its goodness of fit.
Database -Census Bureau
Evaluation Metrics Results Such as Accuracy, Recall, Precision, and F1score were calculated.
#StochasticGradientDecent ,#SGD ,#RandomForest ,#Matrix ,# Vector ,#Accuracy ,#Precision ,#Recall ,#F-Score, # Classifier, # Optimizer, # decision, # binary, # clinical, # variables, # techniques, # overfitting, # data, # reliable, # research, # describes, # applications, # science, # demonstrated, # problems, # discover, # eyecatching, # building, # Summing, # uncorrelated.
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