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The optimization algorithm is an extension ofa coordinate descent methodfollowing a similar path proposed in an earlierpaper. It is usually much faster thanL-BFGSandtruncated Newton methodsfor large-scale and sparse data sets. This class usesempirical risk minimization(i.e., ERM) to formulate the...
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# Split data into training and testing sets X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)# Standardize features by removing the mean and scaling to unit variance scaler=StandardScaler()X_train_scaled=scaler.fit_transform(X_train)X_test_scaled=scaler.tr...
# Split data into trainingandtesting sets X_train, X_test, y_train,y_test=train_test_split(X, y,test_size=0.3,random_state=42) # Standardize features by removing the meanandscalingtounit variancescaler=StandardScaler()X_train_scaled=scaler.fit_transform(X_train)X_test_scaled=scaler.transfor...
# Split data into training and testing sets X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42) # Standardize features by removing the mean and scaling to unit variance scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) ...
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