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sgd python

Innocent Iguana answered on January 9, 2024 Popularity 4/10 Helpfulness 6/10

answer sgd python

sgd python

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# Method 1 : SVM (Support Vector Machine)
from sklearn.svm import LinearSVC, SVC 
svm = LinearSVC(class_weight='balanced', random_state=42, loss="hinge", fit_intercept=False) 
svm_alternative = SVC(kernel='linear', gamma=.5, probability=True) # Alternative 1

# in contrast to scikit-learn's LinearSVC, Snap ML offers multi-threaded CPU/GPU training of SVMs
from snapml import SupportVectorMachine
snapml_svm_cpu = SupportVectorMachine(class_weight='balanced', n_jobs=4, fit_intercept=False) # Alternative 2 
snapml_svm_gpu = SupportVectorMachine(class_weight='balanced', use_gpu=True, fit_intercept=False) # Alternative 3

# Method 2 : SGD (Stochastic Gradient Descent)
from sklearn.linear_model import SGDClassifier
sgd = SGDClassifier(loss='hinge', class_weight='balanced', random_state=42, fit_intercept=False)

# Measure training time
t0 = time.time()
model.fit(X_train, y_train)
train_time = time.time() - t0

# Predict
y_pred = model.predict(X_test)

# Evaluate model
roc_auc_score(y_test, y_pred)

# Get confidence score for probability
y_pred_conf = model.decision_function(X_test)

# Evaluate hinge loss
hinge_loss(y_test, y_pred_conf)

Popularity 4/10 Helpfulness 6/10 Language python

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Contributed on Jan 09 2024

Innocent Iguana

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