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covid_analysis
Commit 720e4ae1 authored by Joaquin Torres's avatar Joaquin Torres
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Ready to re run everything

parent 1977e62f
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model_selection/hyperparam_tuning.py
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...@@ -72,22 +72,22 @@ if __name__ == "__main__": ...@@ -72,22 +72,22 @@ if __name__ == "__main__":
# -------------------------------------------------------------------------------------------------------- # --------------------------------------------------------------------------------------------------------
# 1. No class weight # 1. No class weight
models_simple = {"DT" : DecisionTreeClassifier(), models_simple = {"DT" : DecisionTreeClassifier(),
# "RF" : RandomForestClassifier(), "RF" : RandomForestClassifier(),
# "Bagging" : BaggingClassifier(), "Bagging" : BaggingClassifier(),
# "AB" : AdaBoostClassifier(algorithm='SAMME'), "AB" : AdaBoostClassifier(algorithm='SAMME'),
# "XGB": XGBClassifier(), "XGB": XGBClassifier(),
# "LR" : LogisticRegression(max_iter=1000), "LR" : LogisticRegression(max_iter=1000),
# "SVM" : SVC(probability=True, max_iter=1000), "SVM" : SVC(probability=True, max_iter=1000),
# "MLP" : MLPClassifier(max_iter=500) "MLP" : MLPClassifier(max_iter=500)
} }
# 2. Class weight: cost-sensitive learning # 2. Class weight: cost-sensitive learning
models_CS = {"DT" : DecisionTreeClassifier(class_weight='balanced'), models_CS = {"DT" : DecisionTreeClassifier(class_weight='balanced'),
# "RF" : RandomForestClassifier(class_weight='balanced'), "RF" : RandomForestClassifier(class_weight='balanced'),
# "Bagging" : BaggingClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')), "Bagging" : BaggingClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')),
# "AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'), "AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),
# "LR" : LogisticRegression(max_iter=1000, class_weight='balanced'), "LR" : LogisticRegression(max_iter=1000, class_weight='balanced'),
# "SVM" : SVC(probability=True, max_iter = 1000, class_weight='balanced'), "SVM" : SVC(probability=True, max_iter = 1000, class_weight='balanced')
} }
# -------------------------------------------------------------------------------------------------------- # --------------------------------------------------------------------------------------------------------
......
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