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11/07/2020· from sklearn.model_selection import train_test_split as tts. X_train, X_test, y_train, y_test = tts Step
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16/05/2018· Random forests has a variety of applications, such as recommendation engines, image classification and feature selection. It can be used to classify loyal loan applicants, identify fraudulent activity and predict diseases. It lies at the base of the Boruta algorithm, which selects important features in a dataset.
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3. Support Vector Machine. This algorithm plays a vital role in Classification problems, and most popularly, machine learning supervised algorithms. Its an important tool used by the researcher and data scientist. This SVM is very easy, and its process is to find a hyperplane in an N dimensional space data point.
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