![]() ![]() Tree-based models are much more robust to outliers than linear models, and they do not need variables to be normalized to work. To fit and train this model, we’ll be following The Machine Learning Workflow infographic however, as our data is pretty clean, we won’t be carrying out every step. The following packages and functions are used in this tutorial: # Data Processingįrom sklearn.ensemble import RandomForestClassifierįrom trics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplayįrom sklearn.model_selection import RandomizedSearchCV, train_test_split
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