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4 changes: 2 additions & 2 deletions docs/source/shallow_learning_methods/logistic_regression.rst
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Expand Up @@ -103,9 +103,9 @@ Example Usage
dataset = dataset.to_numpy()
attributes = dataset[:, 0:5] # Columns 1-5 contain our features
labels = dataset[:, 5] # Column 6 contains our class labels
log_model = LinRegression(attributes, labels)
log_model = LogRegression(attributes, labels)
log_model.run() # This will trigger the command-line interface for parameter input

new_data_X = read_csv('/path/to/new_data_X.csv')
new_data_X = new_data_X.to_numpy()
classifications = log_model.predict(new_data_X) # This will return and output classifications for new_data_X
classifications = log_model.predict(new_data_X) # This will return and output classifications for new_data_X