sample classification pipeline with hyperparameter tuning

josh.ipynb answered on June 7, 2022 Popularity 3/10 Helpfulness 2/10

answer sample classification pipeline with hyperparameter tuning

related sample regression algorithm using pipeline with hyperparameter tuning

sample classification pipeline with hyperparameter tuning

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# Setup the pipeline
steps = [('scaler', StandardScaler()),
         ('SVM', SVC())]

pipeline = Pipeline(steps)

# Specify the hyperparameter space
parameters = {'SVM__C':[1, 10, 100],
              'SVM__gamma':[0.1, 0.01]}

…# Predict the labels of the test set: y_pred
y_pred = cv.predict(X_test)

# Compute and print metrics
print("Accuracy: {}".format(cv.score(X_test, y_test)))
print(classification_report(y_test, y_pred))
print("Tuned Model Parameters: {}".format(cv.best_params_))

Popularity 3/10 Helpfulness 2/10 Language python

Source: campus.datacamp.com

Tags: classification pipeline python

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Contributed on Jun 07 2022

josh.ipynb

0 Answers Avg Quality 2/10

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sample regression algorithm using pipeline with hyperparameter tuning

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# Setup the pipeline steps: steps
steps = [('imputation', Imputer(missing_values='NaN', strategy='mean', axis=0)),
         ('scaler', StandardScaler()),
         ('elasticnet', ElasticNet())]
         
# Create the pipeline: pipeline
pipeline = Pipeline(steps)

# Specify the hyperparameter space
parameters = {'elasticnet__l1_ratio':np.linspace(0,1,30)}
…
# Compute and print the metrics
r2 = gm_cv.score(X_test, y_test)
print("Tuned ElasticNet Alpha: {}".format(gm_cv.best_params_))
print("Tuned ElasticNet R squared: {}".format(r2))

Popularity 3/10 Helpfulness 1/10 Language python

Source: campus.datacamp.com

Tags: regression python using pipeline algorithm

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Contributed on Jun 07 2022

josh.ipynb

0 Answers Avg Quality 2/10

sample classification pipeline with hyperparameter tuning

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sample regression algorithm using pipeline with hyperparameter tuning

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