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first version of the plot and some noose

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Maximilian Kany
2025-08-19 13:24:47 +02:00
parent 530d312dfd
commit c0e6995b6d
1 changed files with 5 additions and 4 deletions
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9
compare_models_10k.py
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@@ -28,7 +28,7 @@ from sklearn.neural_network import MLPClassifier
set_config(transform_output="pandas") # dataframe supremacy set_config(transform_output="pandas") # dataframe supremacy
jobs = 12 jobs = 3
max_iter = 3000 max_iter = 3000
def prepDataset(dataset): #returns X_train, X_test, y_train, y_test def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
@@ -78,6 +78,7 @@ def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
#print((mask == False).sum()) #31 cases with all target columns 0 #print((mask == False).sum()) #31 cases with all target columns 0
X_clean = X[mask] X_clean = X[mask]
y_clean = y[mask] y_clean = y[mask]
print(y_clean)
# Split dataset # Split dataset
return train_test_split(X_clean, y_clean, random_state=0) return train_test_split(X_clean, y_clean, random_state=0)
def comparison(X_train, X_test, y_train, y_test, estimator,): #returns class_report def comparison(X_train, X_test, y_train, y_test, estimator,): #returns class_report
@@ -93,9 +94,9 @@ datasets = [
#'games_march2025_cleaned.csv' #'games_march2025_cleaned.csv'
] ]
estimators = { estimators = {
"RidgeClassifier": RidgeClassifier(random_state=0, max_iter=max_iter), #"RidgeClassifier": RidgeClassifier(random_state=0, max_iter=max_iter),
"PassiveAggressiveClassifier": PassiveAggressiveClassifier(random_state=0, max_iter=max_iter), #"PassiveAggressiveClassifier": PassiveAggressiveClassifier(random_state=0, max_iter=max_iter),
"Perceptron": Perceptron(random_state=0, max_iter=max_iter), #"Perceptron": Perceptron(random_state=0, max_iter=max_iter),
"SGDClassifier": SGDClassifier(random_state=0, max_iter=max_iter), "SGDClassifier": SGDClassifier(random_state=0, max_iter=max_iter),
"NearestCentroid": NearestCentroid(), "NearestCentroid": NearestCentroid(),
"LinearSVC": LinearSVC(random_state=0, max_iter=max_iter), "LinearSVC": LinearSVC(random_state=0, max_iter=max_iter),