Verschiedene Ergebnisse (mit Samen) für Sklearn -Zufallswald ⇐ Python

[Guest]

Ich benutze Sklearn, um einen zufälligen Wald zu betreiben. Ich setze den Saatgut für den zufälligen Wald sowie die Daten zur Kreuzvalidierung auf. Wenn ich die Code aufeinanderfolgende Zeiten erneut ausführe, gibt es mir das gleiche Ergebnis. Nach einem Monat habe ich jedoch den gleichen Code wieder auftreten und habe etwas andere Funktionen für Features. In einigen anderen ähnlichen Analysen sind auch die Genauigkeitsmetriken unterschiedlich. Die Daten wurden nicht geändert. Ich laufe auf Google Colab. < /P>
Hier ist mein Code: < /p>

Code: Select all

# Configuration
file_path = '/content/drive/My Drive/dataset.csv'
columns_to_keep = [
'target_column', 'feature_a', 'feature_b', 'feature_c', 'feature_d', 'feature_e',
'feature_f', 'feature_g', 'feature_h', 'feature_i', 'feature_j', 'feature_k', 'feature_l',
'feature_m', 'feature_n', 'feature_o', 'feature_p', 'feature_q', 'feature_r', 'feature_s',
'feature_t', 'feature_u', 'feature_v', 'feature_w', 'feature_x', 'feature_y', 'feature_z',
'feature_aa', 'feature_ab', 'feature_ac', 'feature_ad', 'feature_ae', 'feature_af', 'feature_ag',
'feature_ah', 'feature_ai', 'feature_aj', 'feature_ak', 'feature_al', 'feature_am', 'feature_an'
]

df = pd.read_csv(file_path, usecols=columns_to_keep)

categorical_columns = ['feature_ak', 'feature_al', 'feature_am', 'feature_an', 'feature_ao']
one_hot_columns = ['feature_al', 'feature_ak']

df = df.dropna()

# One-hot encode the specified column
le = LabelEncoder()
for col in one_hot_columns:
df[col] = le.fit_transform(df[col])

# Convert specified columns to categorical
for col in categorical_columns:
df[col] = df[col].astype('category')

# Split into features and target
X = df.drop(columns=['target_column'])
y = df['target_column']

# Initialize the RandomForestClassifier model
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)

# Initialize k-fold cross-validation
kf = KFold(n_splits=5, shuffle=True, random_state=42)

# Store results
feature_importances_list = []
all_y_true = []
all_y_pred = []

# Perform k-fold cross-validation
for fold_num, (train_index, test_index) in enumerate(kf.split(X), start=1):
# Split the data
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]

# Train the Random Forest model
rf_model.fit(X_train, y_train)

# Predict on the test set
y_pred = rf_model.predict(X_test)

# Collect all true and predicted labels
all_y_true.extend(y_test)
all_y_pred.extend(y_pred)

# Get feature importances for this fold
feature_importances_list.append(rf_model.feature_importances_)

# Calculate and print the accuracy for this fold
accuracy_fold = accuracy_score(y_test, y_pred)
print(f"Fold {fold_num} Accuracy: {accuracy_fold:.4f}")

# Calculate accuracy across all predictions
accuracy_cv = accuracy_score(all_y_true, all_y_pred)

# Generate a classification report
final_report = classification_report(all_y_true, all_y_pred, digits=3)

# Average feature importances across folds
average_importance = sum(feature_importances_list) / len(feature_importances_list)

# Create a DataFrame with feature names and their corresponding average importance
feature_names = X.columns
importance_df = pd.DataFrame({
'Feature': feature_names,
'Importance': average_importance
}).sort_values(by='Importance', ascending=False)

# Print Results
print(f"Overall Accuracy of Random Forest model with k-fold CV: {accuracy_cv:.4f}")

print("\nFinal Classification Report:")
print(final_report)

print("\nRandom Forest Feature Importances (averaged across folds):")
print(importance_df.head(20))