from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix from sklearn.model_selection import train_test_split import pandas as pd # Train a classifier model def train_model(df: pd.DataFrame, target_column: str, model_type: str = "logistic_regression"): """ Trains a model on the dataset using the specified model type. Args: - df (pd.DataFrame): The dataset. - target_column (str): The target column for prediction. - model_type (str): Type of model ('logistic_regression' or 'random_forest'). Returns: - model: The trained model. """ X = df.drop(columns=[target_column]) y = df[target_column] # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) if model_type == "logistic_regression": model = LogisticRegression() elif model_type == "random_forest": model = RandomForestClassifier(n_estimators=100) else: raise ValueError(f"Unsupported model type: {model_type}") # Train the model model.fit(X_train, y_train) # Predict and evaluate model y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) cm = confusion_matrix(y_test, y_pred) print(f"Model Accuracy: {accuracy}") print(f"Confusion Matrix:\n{cm}") return model # Model evaluation with custom metrics (e.g., precision, recall, F1-score) def evaluate_model(model, X_test: pd.DataFrame, y_test: pd.Series): """ Evaluates a trained model using custom metrics. Args: - model: The trained model. - X_test (pd.DataFrame): The test feature data. - y_test (pd.Series): The true labels. Returns: - dict: Dictionary containing custom evaluation metrics. """ from sklearn.metrics import classification_report y_pred = model.predict(X_test) report = classification_report(y_test, y_pred, output_dict=True) return report