diff --git a/.gitignore b/.gitignore
index 6ce2773f..08ea1217 100644
--- a/.gitignore
+++ b/.gitignore
@@ -51,3 +51,6 @@ tests/integration/attacks/tartan_federer/assets/tartan_federer_attack_results
 # Training Logs
 *.err
 *.out
+
+# CatBoost training artifacts
+catboost_info/
diff --git a/examples/common/utils.py b/examples/common/utils.py
index c8c27137..8a04571d 100644
--- a/examples/common/utils.py
+++ b/examples/common/utils.py
@@ -4,7 +4,9 @@
 from pathlib import Path
 
 
-def iterate_model_folders(input_data_path: Path, diffusion_model_names: list[str]) -> Generator[tuple[str, Path, str]]:
+def iterate_model_folders(
+    input_data_path: Path, diffusion_model_names: list[str]
+) -> Generator[tuple[str, Path, str, str]]:
     """
     Iterates over the competition's shadow model folder structure and yields model information.
 
@@ -13,7 +15,8 @@ def iterate_model_folders(input_data_path: Path, diffusion_model_names: list[str
         diffusion_model_names: A list of diffusion model names to iterate over.
 
     Yields:
-        A tuple containing the model name, the path to the model's data, and the model folder name.
+        A tuple containing the model name (e.g. tabddpm), the path to the model's data,
+        the model folder name (e.g. tabddpm_1), and mode (train, dev, final).
     """
     modes = ["train", "dev", "final"]
     for model_name in diffusion_model_names:
@@ -25,4 +28,4 @@ def iterate_model_folders(input_data_path: Path, diffusion_model_names: list[str
 
             model_folders = [entry for entry in current_path.iterdir() if entry.is_dir()]
             for model_folder_path in model_folders:
-                yield model_name, model_folder_path, model_folder_path.name
+                yield model_name, model_folder_path, model_folder_path.name, mode
diff --git a/examples/ept_attack/config.yaml b/examples/ept_attack/config.yaml
index f7b06a07..a4009858 100644
--- a/examples/ept_attack/config.yaml
+++ b/examples/ept_attack/config.yaml
@@ -1,6 +1,6 @@
 # Ensemble example configuration
 # Base data directory (can be overridden from command line)
-base_data_dir: examples/ept_attack/data/
+base_data_dir: examples/ept_attack/data
 base_example_dir: examples/ept_attack
 
 # Data paths (relative to base_data_dir)
@@ -8,13 +8,16 @@ data_paths:
   input_data_path: ${base_data_dir}/midst_data_black_box_attacks # Read-only input data directory
   output_data_path: ${base_data_dir}/output # Directory to save processed data and results
   data_types_file_path: ${base_data_dir}/data_configs/data_types.json # Path to the JSON file defining column types
-
+  attribute_features_path: ${data_paths.output_data_path}/attribute_prediction_features # Path to save attribute prediction features
 # Pipeline control
 pipeline:
   run_data_processing: false # Whether to run data processing
   run_shadow_model_training: false # Whether to run shadow model training
-  run_feature_extraction: true # Whether to run attribute prediction model training
-  run_attack_classifier_training: false # Whether to run attack classifier training
+  run_feature_extraction: false # Whether to run attribute prediction model training
+  run_attack_classifier_training: true # Whether to run attack classifier training
+
+classifier_settings:
+  results_output_path: ${data_paths.output_data_path}/evaluation_ML
 
 attack_settings:
   single_table: true # Whether the data is single-table
diff --git a/examples/ept_attack/run_ept_attack.py b/examples/ept_attack/run_ept_attack.py
index a8accf81..292e63c6 100644
--- a/examples/ept_attack/run_ept_attack.py
+++ b/examples/ept_attack/run_ept_attack.py
@@ -6,17 +6,23 @@
 
 """
 
+import itertools
 import json
+from datetime import datetime
 from logging import INFO
 from pathlib import Path
+from typing import Any
 
 import hydra
+import pandas as pd
 from omegaconf import DictConfig
 
 from examples.common.utils import iterate_model_folders
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe, save_dataframe
+from midst_toolkit.attacks.ept.classification import train_attack_classifier
 from midst_toolkit.attacks.ept.feature_extraction import extract_features
 from midst_toolkit.common.logger import log
+from midst_toolkit.common.random import set_all_random_seeds
 
 
 # Step 2 and 3: Attribute prediction model training and feature extraction
@@ -34,7 +40,7 @@ def run_attribute_prediction(config: DictConfig) -> None:
 
     diffusion_model_names = ["tabddpm", "tabsyn"] if config.attack_settings.single_table else ["clavaddpm"]
     input_data_path = Path(config.data_paths.input_data_path)
-    output_features_path = Path(config.data_paths.output_data_path, "attribute_prediction_features")
+    output_features_path = Path(config.data_paths.attribute_features_path)
 
     # Load column types specific to the competition dataset
     with open(config.data_paths.data_types_file_path, "r") as f:
@@ -46,8 +52,16 @@ def run_attribute_prediction(config: DictConfig) -> None:
         "categorical": [col for col in column_types.get("categorical", []) if not col.endswith("_id")],
     }
 
+    # Assert that the input data path exists and is not empty
+    assert input_data_path.exists() and input_data_path.is_dir(), f"Input data directory not found: {input_data_path}"
+    assert any(input_data_path.iterdir()), f"Input data directory is empty: {input_data_path}"
+
     # Iterating over directories specific to the shadow models folder structure in the competition
-    for model_name, model_data_path, model_folder in iterate_model_folders(input_data_path, diffusion_model_names):
+    for model_name, model_data_path, model_folder, mode in iterate_model_folders(
+        input_data_path, diffusion_model_names
+    ):
+        log(INFO, f"Processing model: {model_name}, path: {model_data_path}, folder: {model_folder}, mode: {mode}")
+
         # Load the data files as dataframes
         df_synthetic_data = load_dataframe(model_data_path, "trans_synthetic.csv")
         df_challenge_data = load_dataframe(model_data_path, "challenge_with_id.csv")
@@ -65,7 +79,7 @@ def run_attribute_prediction(config: DictConfig) -> None:
             random_seed=config.random_seed,
         )
 
-        final_output_dir = output_features_path / f"{model_name}_black_box"
+        final_output_dir = output_features_path / f"{model_name}_black_box" / mode
 
         final_output_dir.mkdir(parents=True, exist_ok=True)
 
@@ -73,9 +87,163 @@ def run_attribute_prediction(config: DictConfig) -> None:
         model_folder_number = int(model_folder.split("_")[-1])
         file_name = f"attribute_prediction_features_{model_folder_number}.csv"
 
+        if mode == "train":
+            file_name = f"attribute_prediction_features_with_labels_{model_folder_number}.csv"
+
+            # Load the challenge labels and add them to the features dataframe
+            df_labels = load_dataframe(model_data_path, "challenge_label.csv")
+
+            # Check that the number of rows align
+            assert len(df_extracted_features) == len(df_labels), (
+                f"The number of rows in the extracted features ({len(df_extracted_features)}) "
+                f"does not match the number of labels ({len(df_labels)})."
+            )
+            df_extracted_features["is_train"] = df_labels.values
+
         save_dataframe(df=df_extracted_features, file_path=final_output_dir, file_name=file_name)
 
 
+# Step 4: Attack classifier training
+def run_attack_classifier_training(config: DictConfig) -> None:
+    """
+    Trains multiple attack classifiers to distinguish between training and synthetic data,
+    and selects the best performing configuration based on evaluation metrics.
+
+    This function orchestrates the training of various attack classifiers (XGBoost,
+    CatBoost, MLP) to perform a membership inference attack. It iterates through
+    different diffusion models used to generate synthetic data and all combinations
+    of feature types derived from the attribute prediction task.
+
+    The process involves:
+    1.  Reading pre-computed feature files generated by the feature extraction step.
+    2.  Splitting the feature files into training and testing sets.
+    3.  For each diffusion model, iterating through all possible combinations of
+        feature columns ('actual', 'error', 'error_ratio', 'accuracy', 'prediction').
+    4.  Training each classifier type on these feature combinations.
+    5.  Evaluating the classifier's performance and saving the scores (e.g., AUC, TPR at
+        specific FPR) and prediction results for each configuration.
+    6.  Aggregating all results into a summary CSV file, which includes a final
+        metric ('final_tpr_fpr_10') representing the best TPR at 10% FPR across
+        all diffusion models for a given classifier and feature set.
+    7.  Logging the best-performing attack configuration based on this final metric.
+
+    Args:
+        config: Configuration object set in config.yaml.
+    """
+    log(INFO, "Running attack classifier training.")
+
+    data_format, diffusion_models = (
+        ("single_table", ["tabddpm", "tabsyn"])
+        if config.attack_settings.single_table
+        else ("multi_table", ["clavaddpm"])
+    )
+
+    # Read all the files from the attribute prediction features directory
+    features_data_path = Path(config.data_paths.attribute_features_path)
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    summary_results: list[dict[str, Any]] = []
+
+    for diffusion_model_name in diffusion_models:
+        train_features_path = features_data_path / f"{diffusion_model_name}_black_box" / "train"
+
+        assert train_features_path.exists() and train_features_path.is_dir(), (
+            f"Directory not found: {train_features_path}. Make sure to run feature extraction first."
+        )
+        assert any(train_features_path.iterdir()), (
+            f"Directory is empty: {train_features_path}. Make sure to run feature extraction first."
+        )
+
+        sorted_feature_files = sorted(train_features_path.glob("*.csv"))
+
+        # Get the first 25 feature files
+        train_feature_files = sorted_feature_files[:25]
+        # Concatenate all the train feature files into a single dataframe
+        df_train_features = pd.concat([pd.read_csv(f) for f in train_feature_files], ignore_index=True)
+        train_labels = df_train_features["is_train"]
+        df_train_features = df_train_features.drop(columns=["is_train"])
+
+        test_feature_files = sorted_feature_files[25:]
+        df_test_features = pd.concat([pd.read_csv(f) for f in test_feature_files], ignore_index=True)
+        test_labels = df_test_features["is_train"]
+        df_test_features = df_test_features.drop(columns=["is_train"])
+
+        classifier_types = ["XGBoost", "CatBoost", "MLP"]
+        column_types = ["actual", "error", "error_ratio", "accuracy", "prediction"]
+
+        output_summary_path = Path(config.classifier_settings.results_output_path) / data_format / f"{timestamp}_train"
+        output_summary_path.mkdir(parents=True, exist_ok=True)
+
+        for classifier in classifier_types:  # XGBoost, CatBoost, MLP
+            for r in range(1, len(column_types) + 1):
+                for selected_columns_tuple in itertools.combinations(column_types, r):
+                    # Find if a result for this combination already exists
+                    columns_str = " ".join(sorted(selected_columns_tuple))
+                    row = next(
+                        (
+                            item
+                            for item in summary_results
+                            if item["classifier"] == classifier and item["columns_lst"] == columns_str
+                        ),
+                        None,
+                    )
+
+                    if not row:
+                        row = {"classifier": classifier, "columns_lst": columns_str}
+                        summary_results.append(row)
+
+                    results = train_attack_classifier(
+                        classifier_type=classifier,
+                        columns_list=list(selected_columns_tuple),
+                        x_train=df_train_features,
+                        y_train=train_labels,
+                        x_test=df_test_features,
+                        y_test=test_labels,
+                    )
+
+                    # Update row with scores for the current diffusion model
+                    for score_name, score_value in results["scores"].items():
+                        # Sanitize score_name for column header
+                        col_name = score_name.lower().replace(" ", "_").replace("-", "_")
+                        col_name = col_name.replace("_at_", "_").replace(".0", "")
+                        row[f"{diffusion_model_name}_{col_name}"] = score_value
+
+                    training_directory_name = f"{classifier}_" + "_".join(selected_columns_tuple)
+                    training_output_path = output_summary_path / training_directory_name
+                    training_output_path.mkdir(parents=True, exist_ok=True)
+
+                    # Save prediction results
+                    prediction_results_df = results["prediction_results"]
+                    prediction_results_file_name = f"{diffusion_model_name}_prediction_results.csv"
+                    save_dataframe(
+                        df=pd.DataFrame(prediction_results_df),
+                        file_path=training_output_path,
+                        file_name=prediction_results_file_name,
+                    )
+
+                    # Save scores
+                    scores_file_name = f"{diffusion_model_name}_results.txt"
+                    with open(training_output_path / scores_file_name, "w") as f:
+                        for score_name, score_value in results["scores"].items():
+                            f.write(f"{score_name}: {score_value}\n")
+
+    summary_file_name = "attack_classifier_summary.csv"
+    summary_df = pd.DataFrame(summary_results)
+
+    # Add final_tpr_fpr_10 column which is the max TPR at FPR=10% across diffusion models
+    tpr_10_cols = [col for col in summary_df.columns if col.endswith("_tpr_fpr_10")]
+    if tpr_10_cols:
+        summary_df["final_tpr_fpr_10"] = summary_df[tpr_10_cols].max(axis=1)
+
+    summary_df.to_csv(output_summary_path / summary_file_name, index=False)
+
+    log(INFO, f"Saved attack classifier summary to {output_summary_path / summary_file_name}")
+
+    summary_df.sort_values(by=["final_tpr_fpr_10"], ascending=False, inplace=True)
+    best_result = summary_df.head(1)
+    log(INFO, f"Best performing attack configuration:\n{best_result}")
+
+
 @hydra.main(config_path=".", config_name="config", version_base=None)
 def main(config: DictConfig) -> None:
     """
@@ -88,18 +256,22 @@ def main(config: DictConfig) -> None:
     """
     log(INFO, "Running EPT-MIA Attack Example Pipeline.")
 
+    if config.random_seed is not None:
+        set_all_random_seeds(seed=config.random_seed)
+        log(INFO, f"Training phase random seed set to {config.random_seed}.")
+
     if config.attack_settings.single_table:
         log(INFO, "Data: Single-table.")
     else:
         log(INFO, "Data: Multi-table.")
 
     # TODO: Implement potential data preprocessing step.
-    # TODO: Implement shadow model training step.
 
     if config.pipeline.run_feature_extraction:
         run_attribute_prediction(config)
 
-    # TODO: Implement attack classifier training step.
+    if config.pipeline.run_attack_classifier_training:
+        run_attack_classifier_training(config)
 
 
 if __name__ == "__main__":
diff --git a/mypy.ini b/mypy.ini
index 10466223..9854213e 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -42,3 +42,6 @@ ignore_missing_imports = True
 
 [mypy-faiss.*]
 ignore_missing_imports = True
+
+[mypy-catboost.*]
+ignore_missing_imports = True
diff --git a/pyproject.toml b/pyproject.toml
index f55eb2d2..ee9ad24d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,8 @@ dependencies = [
     "pydantic>=2.12.3",
     "filelock>=3.20.1",
     "sdv>=1.18.0",
+    "catboost>=1.2.8",
+    "scipy==1.16.3",
 ]
 
 [build-system]
diff --git a/src/midst_toolkit/attacks/ept/classification.py b/src/midst_toolkit/attacks/ept/classification.py
new file mode 100644
index 00000000..3738e945
--- /dev/null
+++ b/src/midst_toolkit/attacks/ept/classification.py
@@ -0,0 +1,271 @@
+from logging import INFO
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import torch
+from catboost import CatBoostClassifier
+from sklearn.metrics import accuracy_score, auc, roc_curve
+from torch import nn, optim
+from xgboost import XGBClassifier
+
+from midst_toolkit.common.logger import log
+
+
+def filter_data(features_df: pd.DataFrame, columns_list: list[str]) -> np.ndarray:
+    """
+    Filters columns from a single DataFrame based on specified suffixes.
+
+    This function processes a pandas DataFrame, selecting columns based on
+    suffixes that correspond to the types specified in `columns_list` (e.g.,
+    'actual', 'error'). It then returns the data from these selected columns
+    as a NumPy array.
+
+    Args:
+        features_df: The pandas DataFrame to process.
+        columns_list: A list of strings specifying the types of columns
+                    to select.
+
+    Returns:
+        A NumPy array containing the data from the selected columns.
+    """
+    suffix_mapping = {
+        "actual": lambda x: not (
+            x.endswith("error") or x.endswith("error_ratio") or x.endswith("accuracy") or x.endswith("prediction")
+        ),
+        "error": lambda x: x.endswith("error"),
+        "error_ratio": lambda x: x.endswith("error_ratio"),
+        "accuracy": lambda x: x.endswith("accuracy"),
+        "prediction": lambda x: x.endswith("prediction"),
+    }
+
+    # Filter columns for each type in args.columns_lst
+    selected_columns = [
+        col for col_type in columns_list for col in features_df.columns if suffix_mapping[col_type](col)
+    ]
+
+    return features_df[selected_columns].values
+
+
+class MLPClassifier(nn.Module):
+    def __init__(self, input_size: int = 100, hidden_size: int = 64, output_size: int = 1):
+        """
+        Creates the Multi-layer perceptron classifier. Defines a simple feedforward neural network with
+        customizable input, hidden, and output sizes.
+
+        Args:
+            input_size: The number of features in the input data. Defaults to 100.
+            hidden_size: The number of neurons in the hidden layer. Defaults to 64.
+            output_size: The number of output neurons, typically 1 for binary classification. Defaults to 1.
+        """
+        super(MLPClassifier, self).__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(input_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, output_size), nn.Sigmoid()
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Performs the forward pass of the MLP.
+
+        Args:
+            x: The input tensor.
+
+        Returns:
+            The output tensor after passing through the network.
+        """
+        return self.layers(x)
+
+
+def train_mlp(
+    x_train: np.ndarray,
+    y_train: np.ndarray,
+    x_test: np.ndarray,
+    device: torch.device,
+    eval: bool,
+    epochs: int = 10,
+) -> tuple[np.ndarray | None, np.ndarray | None]:
+    """
+    Trains a simple MLP classifier and optionally evaluates it on a test set.
+
+    Args:
+        x_train: Training data features.
+        y_train: Training data labels.
+        x_test: Test data features.
+        device: The device to train the model on (e.g., 'cpu' or 'cuda').
+        eval: If True, evaluates the model on the test set.
+        epochs: Number of training epochs. Default is 10.
+
+    Returns:
+        A tuple containing:
+        - The predicted labels for the test set (or None if eval is False).
+        - The prediction probabilities for the test set (or None if eval is False).
+    """
+    input_size = x_train.shape[1]
+    model = MLPClassifier(input_size=input_size).to(device)
+    criterion = nn.BCELoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    x_train_tensor, y_train_tensor = (
+        torch.tensor(x_train, dtype=torch.float32).to(device),
+        torch.tensor(y_train, dtype=torch.float32).to(device),
+    )
+
+    if eval:
+        x_test_tensor = torch.tensor(x_test, dtype=torch.float32).to(device)
+
+    # Train the model
+    for _ in range(epochs):
+        model.train()
+        optimizer.zero_grad()
+        outputs = model(x_train_tensor).squeeze()
+        loss = criterion(outputs, y_train_tensor)
+        loss.backward()
+        optimizer.step()
+
+    y_pred, y_proba = None, None
+
+    if eval:
+        model.eval()
+
+        with torch.no_grad():
+            # Get probabilities
+            y_proba = model(x_test_tensor).squeeze().cpu().numpy()
+            # Convert probabilities to binary predictions
+            y_pred = (y_proba > 0.5).astype(float)
+
+    return y_pred, y_proba
+
+
+def get_scores(
+    y_true: np.ndarray,
+    y_proba: np.ndarray,
+    y_pred: np.ndarray,
+    fpr_thresholds: list[float] | None = None,
+) -> dict[str, float]:
+    """
+    Calculates and returns a dictionary of evaluation scores for a binary classifier.
+
+    This function computes the accuracy, the Area Under the Receiver Operating Characteristic
+    Curve (AUC-ROC), and the True Positive Rate (TPR) at specified False Positive Rate (FPR)
+    thresholds.
+
+    Args:
+        y_true: Ground truth binary labels.
+        y_proba: Predicted probabilities for the positive class.
+        y_pred: Predicted binary labels.
+        fpr_thresholds: A list of FPR values at which to calculate the TPR.
+                        Defaults to [0.1, 0.01, 0.001].
+
+    Returns:
+        A dictionary containing the calculated scores: 'accuracy', 'AUC-ROC', and
+        'TPR at FPR {threshold}' for each specified threshold.
+    """
+    if fpr_thresholds is None:
+        fpr_thresholds = [0.1, 0.01, 0.001]
+
+    accuracy = accuracy_score(y_true, y_pred)
+    fpr, tpr, _ = roc_curve(y_true, y_proba)
+    auc_roc = auc(fpr, tpr)
+
+    scores = {"accuracy": accuracy, "AUC-ROC": auc_roc}
+
+    # Compute TPR at specific FPR thresholds
+    for threshold in fpr_thresholds:
+        # Find the highest TPR for FPRs less than the threshold
+        valid_tpr = tpr[fpr < threshold]
+        tpr_at_fpr = valid_tpr.max() if valid_tpr.size > 0 else 0.0
+        scores[f"TPR at FPR {threshold * 100}"] = tpr_at_fpr
+
+    return scores
+
+
+def train_attack_classifier(
+    classifier_type: str,
+    columns_list: list[str],
+    x_train: pd.DataFrame,
+    y_train: pd.Series,
+    x_test: pd.DataFrame,
+    y_test: pd.Series,
+) -> dict[str, dict]:
+    """
+    Trains a specified classifier for a membership inference attack.
+    This function takes training and testing data, selects the subset of features given in
+    the provided column list, and trains a classifier (XGBoost, CatBoost, or MLP) to
+    distinguish between member and non-member data points. It then evaluates the
+    classifier on the test set and returns the prediction results and performance scores.
+
+    Args:
+        classifier_type: The type of classifier to train.
+            Supported values are "XGBoost", "CatBoost", and "MLP".
+        columns_list: A list of column names to be used as features for training the classifier.
+        x_train: The feature data for the training set.
+        y_train: The labels for the training set (membership status).
+        x_test: The feature data for the test set.
+        y_test: The labels for the test set (membership status).
+
+    Returns:
+        A dictionary containing the results. It has two keys:
+            - "prediction_results": A dictionary with the true labels ('y_true'),
+              predicted probabilities ('y_proba'), and predicted labels ('y_pred').
+            - "scores": A dictionary of performance metrics, including accuracy,
+              AUC, and TPR at various FPR thresholds.
+    """
+    log(INFO, f"Training {classifier_type} classifier using features from columns: {columns_list}")
+
+    all_results: dict[str, Any] = {}
+
+    x_train_processed = filter_data(x_train, columns_list)
+    y_train_processed = y_train.to_numpy()
+
+    x_test_processed = filter_data(x_test, columns_list)
+    y_test_processed = y_test.to_numpy()
+
+    assert x_train_processed.shape[0] == y_train_processed.shape[0], (
+        "Mismatch in number of training samples and labels"
+    )
+    assert x_test_processed.shape[0] == y_test_processed.shape[0], "Mismatch in number of test samples and labels"
+    assert x_train_processed.shape[1] == x_test_processed.shape[1], (
+        "Mismatch in number of features between train and test sets"
+    )
+
+    assert classifier_type in ["XGBoost", "CatBoost", "MLP"], f"Unsupported classifier type: {classifier_type}"
+
+    y_pred, y_proba = None, None
+
+    if classifier_type == "XGBoost":
+        model = XGBClassifier()
+        model.fit(x_train_processed, y_train_processed)
+        y_pred = model.predict(x_test_processed)
+        y_proba = model.predict_proba(x_test_processed)[:, 1]
+
+    elif classifier_type == "CatBoost":
+        model = CatBoostClassifier(verbose=0)
+        model.fit(x_train_processed, y_train_processed)
+        y_pred = model.predict(x_test_processed)
+        y_proba = model.predict_proba(x_test_processed)[:, 1]
+
+    elif classifier_type == "MLP":
+        y_pred, y_proba = train_mlp(
+            x_train_processed,
+            y_train_processed,
+            x_test_processed,
+            torch.device("cuda" if torch.cuda.is_available() else "cpu"),
+            eval=True,
+        )
+
+    prediction_results = {
+        "y_true": y_test_processed,
+        "y_proba": y_proba,
+        "y_pred": y_pred,
+    }
+
+    assert y_pred is not None and y_proba is not None, (
+        "Predictions and probabilities should not be None to get scores."
+    )
+
+    fpr_thresholds = [0.1, 0.01, 0.001]
+
+    all_results["prediction_results"] = prediction_results
+    all_results["scores"] = get_scores(y_test_processed, y_proba, y_pred, fpr_thresholds)
+
+    return all_results
diff --git a/tests/unit/attacks/ept_attack/test_classification.py b/tests/unit/attacks/ept_attack/test_classification.py
new file mode 100644
index 00000000..ca4753f1
--- /dev/null
+++ b/tests/unit/attacks/ept_attack/test_classification.py
@@ -0,0 +1,213 @@
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+
+from midst_toolkit.attacks.ept.classification import (
+    MLPClassifier,
+    filter_data,
+    get_scores,
+    train_attack_classifier,
+    train_mlp,
+)
+
+
+@pytest.fixture
+def sample_features_df():
+    # Sample DataFrame for testing filter_data.
+    data = {
+        "feature1_actual": [1, 2, 3],
+        "feature1_error": [0.1, 0.2, 0.3],
+        "feature2_error_ratio": [0.01, 0.02, 0.03],
+        "feature3_accuracy": [0.9, 0.8, 0.7],
+        "feature4_prediction": [1, 0, 1],
+        "another_feature": [4, 5, 6],
+    }
+    return pd.DataFrame(data)
+
+
+@pytest.mark.parametrize(
+    "columns_list, expected_cols",
+    [
+        (["actual"], ["feature1_actual", "another_feature"]),
+        (["error"], ["feature1_error"]),
+        (["error_ratio"], ["feature2_error_ratio"]),
+        (["accuracy"], ["feature3_accuracy"]),
+        (["prediction"], ["feature4_prediction"]),
+        (["error", "accuracy"], ["feature1_error", "feature3_accuracy"]),
+        (
+            ["actual", "prediction"],
+            ["feature1_actual", "another_feature", "feature4_prediction"],
+        ),
+        ([], []),
+    ],
+)
+def test_filter_data(sample_features_df, columns_list, expected_cols):
+    # Tests that filter_data correctly selects columns based on suffixes.
+    result = filter_data(sample_features_df, columns_list)
+    expected = sample_features_df[expected_cols].values
+    np.testing.assert_array_equal(result, expected)
+    assert result.shape == (3, len(expected_cols))
+
+
+def test_mlp_classifier():
+    # Tests the MLPClassifier initialization and forward pass.
+    input_size, hidden_size, output_size = 10, 5, 1
+
+    model = MLPClassifier(input_size, hidden_size, output_size)
+
+    # Check layer types and sizes
+    assert isinstance(model.layers[0], torch.nn.Linear)
+    assert model.layers[0].in_features == input_size
+    assert model.layers[0].out_features == hidden_size
+    assert isinstance(model.layers[2], torch.nn.Linear)
+    assert model.layers[2].in_features == hidden_size
+    assert model.layers[2].out_features == output_size
+
+    # Test forward pass
+    input_tensor = torch.randn(4, input_size)
+    output = model(input_tensor)
+    assert output.shape == (4, output_size)
+    assert torch.all(output >= 0) and torch.all(output <= 1)
+
+
+@patch("midst_toolkit.attacks.ept.classification.MLPClassifier")
+def test_train_mlp(mock_mlp_class):
+    # Tests the train_mlp function's logic and outputs.
+    mock_model = MagicMock()
+    mock_model.parameters.return_value = [torch.nn.Parameter(torch.randn(2, 2))]
+
+    mock_model.return_value = torch.rand(10, 1, requires_grad=True)
+
+    eval_output_mock = MagicMock()
+    eval_output_mock.squeeze.return_value.cpu.return_value.numpy.return_value = np.array([0.6, 0.4, 0.7])
+
+    mock_model.side_effect = [
+        torch.rand(10, 1, requires_grad=True),  # 1st call (training)
+        eval_output_mock,  # 2nd call (evaluation)
+    ]
+
+    mock_mlp_class.return_value.to.return_value = mock_model
+
+    x_train = np.random.rand(10, 5)
+    y_train = np.random.randint(0, 2, 10)
+    x_test = np.random.rand(3, 5)
+    device = torch.device("cpu")
+
+    # Test with eval=True
+    y_pred, y_proba = train_mlp(x_train, y_train, x_test, device, eval=True, epochs=1)
+    assert y_pred is not None
+    assert y_proba is not None
+    assert y_pred.shape == (3,)
+    assert y_proba.shape == (3,)
+    np.testing.assert_array_equal(y_pred, np.array([1, 0, 1]))
+
+    mock_model.side_effect = [
+        torch.rand(10, 1, requires_grad=True),
+        eval_output_mock,
+    ]
+
+    # Test with eval=False
+    y_pred_no_eval, y_proba_no_eval = train_mlp(x_train, y_train, x_test, device, eval=False, epochs=1)
+    assert y_pred_no_eval is None
+    assert y_proba_no_eval is None
+
+
+def test_get_scores():
+    # Tests the get_scores function with known values.
+    y_true = np.array([1, 0, 1, 0, 1, 0])
+    y_pred = np.array([1, 1, 1, 0, 0, 0])
+    y_proba = np.array([0.9, 0.8, 0.7, 0.3, 0.2, 0.1])
+    fpr_thresholds = [0.5, 0.1]
+
+    scores = get_scores(y_true, y_proba, y_pred, fpr_thresholds)
+
+    # Accuracy = (2 TP + 2 TN) / 6 = 4/6
+    assert scores["accuracy"] == pytest.approx(4 / 6)
+
+    # AUC-ROC = 2/3 for this specific arrangement
+    assert scores["AUC-ROC"] == pytest.approx(2 / 3)
+
+    # At a threshold of 0.5, FPR is 1/3 and TPR is 2/3. This is the highest TPR for FPR <= 0.5.
+    assert scores["TPR at FPR 50.0"] == pytest.approx(2 / 3)
+
+    # For FPR <= 0.1, max TPR is 1/3 (achieved at actual FPR of 0).
+    assert scores["TPR at FPR 10.0"] == pytest.approx(1 / 3)
+
+
+@pytest.fixture
+def attack_data():
+    # Sample data for training an attack classifier
+    x_train = pd.DataFrame({"feature_error": np.random.rand(20)})
+    y_train = pd.Series(np.random.randint(0, 2, 20))
+    x_test = pd.DataFrame({"feature_error": np.random.rand(10)})
+    y_test = pd.Series(np.random.randint(0, 2, 10))
+    return x_train, y_train, x_test, y_test
+
+
+@pytest.mark.parametrize("classifier_type", ["XGBoost", "CatBoost"])
+@patch("midst_toolkit.attacks.ept.classification.XGBClassifier")
+@patch("midst_toolkit.attacks.ept.classification.CatBoostClassifier")
+def test_train_attack_classifier_tree_models(mock_catboost, mock_xgboost, classifier_type, attack_data):
+    # Tests train_attack_classifier for XGBoost and CatBoost
+    x_train, y_train, x_test, y_test = attack_data
+    columns_list = ["error"]
+
+    mock_model = MagicMock()
+    mock_model.predict.return_value = np.zeros(10)
+    mock_model.predict_proba.return_value = np.zeros((10, 2))
+    if classifier_type == "XGBoost":
+        mock_xgboost.return_value = mock_model
+    else:
+        mock_catboost.return_value = mock_model
+
+    results = train_attack_classifier(classifier_type, columns_list, x_train, y_train, x_test, y_test)
+
+    assert "prediction_results" in results
+    assert "scores" in results
+    assert "y_true" in results["prediction_results"]
+    mock_model.fit.assert_called_once()
+    mock_model.predict.assert_called_once()
+    mock_model.predict_proba.assert_called_once()
+
+
+def test_train_attack_classifier_mismatched_data(attack_data):
+    # Tests that train_attack_classifier raises errors for mismatched data shapes
+    x_train, y_train, x_test, y_test = attack_data
+    columns_list = ["error"]
+
+    # Test mismatches
+    with pytest.raises(AssertionError, match="Mismatch in number of training samples and labels"):
+        train_attack_classifier("XGBoost", columns_list, x_train.head(10), y_train, x_test, y_test)
+
+    with pytest.raises(AssertionError, match="Mismatch in number of test samples and labels"):
+        train_attack_classifier("XGBoost", columns_list, x_train, y_train, x_test.head(5), y_test)
+
+    x_test_wrong_features = x_test.rename(columns={"feature_error": "another_feature"})
+    with pytest.raises(AssertionError, match="Mismatch in number of features between train and test sets"):
+        train_attack_classifier("XGBoost", columns_list, x_train, y_train, x_test_wrong_features, y_test)
+
+
+@patch("midst_toolkit.attacks.ept.classification.train_mlp")
+def test_train_attack_classifier_mlp(mock_train_mlp, attack_data):
+    # Tests train_attack_classifier for the MLP model
+    x_train, y_train, x_test, y_test = attack_data
+    columns_list = ["error"]
+    mock_train_mlp.return_value = (np.zeros(10), np.zeros(10))
+
+    results = train_attack_classifier("MLP", columns_list, x_train, y_train, x_test, y_test)
+
+    assert "prediction_results" in results
+    assert "scores" in results
+    mock_train_mlp.assert_called_once()
+
+    assert mock_train_mlp.call_args[1]["eval"] is True
+
+
+def test_train_attack_classifier_unsupported(attack_data):
+    # Tests that an unsupported classifier type raises an assertion error
+    x_train, y_train, x_test, y_test = attack_data
+    with pytest.raises(AssertionError, match="Unsupported classifier type: SVM"):
+        train_attack_classifier("SVM", ["error"], x_train, y_train, x_test, y_test)
diff --git a/tests/unit/evaluation/quality/test_mean_f1_score_difference.py b/tests/unit/evaluation/quality/test_mean_f1_score_difference.py
index b015c4d7..5c4898f5 100644
--- a/tests/unit/evaluation/quality/test_mean_f1_score_difference.py
+++ b/tests/unit/evaluation/quality/test_mean_f1_score_difference.py
@@ -46,11 +46,11 @@ def test_mean_f1_score_diff_with_preprocess() -> None:
     score = metric.compute(real_data, synthetic_data)
     # Due to numerical fluctuations on github runners, we have slightly different values.
     if is_apple_silicon():
-        assert pytest.approx(0.7668, abs=1e-8) == score["random_forest_real_train_f1"]
-        assert pytest.approx(-0.06789999999999999, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(0.764, abs=1e-8) == score["random_forest_real_train_f1"]
+        assert pytest.approx(-0.0 - 0.06719999999999998, abs=1e-8) == score["mean_f1_difference"]
     else:
-        assert pytest.approx(0.7684, abs=1e-8) == score["random_forest_real_train_f1"]
-        assert pytest.approx(-0.06829999999999997, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(0.7656, abs=1e-8) == score["random_forest_real_train_f1"]
+        assert pytest.approx(-0.06759999999999997, abs=1e-8) == score["mean_f1_difference"]
     assert pytest.approx(0.5008, abs=1e-8) == score["random_forest_synthetic_train_f1"]
     assert pytest.approx(0.5, abs=1e-8) == score["adaboost_real_train_f1"]
     assert pytest.approx(0.49879999999999997, abs=1e-8) == score["adaboost_synthetic_train_f1"]
@@ -76,9 +76,9 @@ def test_mean_f1_score_diff_with_no_categorical() -> None:
     score = metric.compute(real_data, synthetic_data)
     # Due to numerical fluctuations on github runners, we have slightly different values.
     if is_apple_silicon():
-        assert pytest.approx(-0.0792, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(-0.0 - 0.07909999999999996, abs=1e-8) == score["mean_f1_difference"]
     else:
-        assert pytest.approx(-0.0795, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(-0.0794, abs=1e-8) == score["mean_f1_difference"]
     unset_all_random_seeds()
 
 
@@ -99,11 +99,11 @@ def test_mean_f1_score_diff_with_holdout_difference_f1() -> None:
     score = metric.compute(real_data, synthetic_data, holdout_data)
     # Due to numerical fluctuations on github runners, we have slightly different values.
     if is_apple_silicon():
-        assert pytest.approx(0.7667172638761903, abs=1e-8) == score["random_forest_real_train_f1"]
-        assert pytest.approx(-0.17912194553312424, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(0.763919128428235, abs=1e-8) == score["random_forest_real_train_f1"]
+        assert pytest.approx(-0.1784224116711354, abs=1e-8) == score["mean_f1_difference"]
     else:
-        assert pytest.approx(0.7683679496293229, abs=1e-8) == score["random_forest_real_train_f1"]
-        assert pytest.approx(-0.1795346169714074, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(0.7655658771879633, abs=1e-8) == score["random_forest_real_train_f1"]
+        assert pytest.approx(-0.17883409886106752, abs=1e-8) == score["mean_f1_difference"]
     assert pytest.approx(0.40831722022666145, abs=1e-8) == score["random_forest_synthetic_train_f1"]
     assert pytest.approx(0.3632940727026944, abs=1e-8) == score["adaboost_real_train_f1"]
     assert pytest.approx(0.33490261584802905, abs=1e-8) == score["adaboost_synthetic_train_f1"]
diff --git a/tests/unit/evaluation/quality/test_mean_regression_difference.py b/tests/unit/evaluation/quality/test_mean_regression_difference.py
index 97cd19b4..3d4faff3 100644
--- a/tests/unit/evaluation/quality/test_mean_regression_difference.py
+++ b/tests/unit/evaluation/quality/test_mean_regression_difference.py
@@ -104,9 +104,9 @@ def test_mean_regression_diff_with_no_categorical() -> None:
     score = metric.compute(real_data, synthetic_data, holdout_data)
     # Due to numerical fluctuations on github runners, we have slightly different values.
     if is_apple_silicon():
-        assert pytest.approx(-0.0565015943519761, abs=1e-8) == score["RandomForestRegressor_r2_difference"]
+        assert pytest.approx(-0.05650138480537015, abs=1e-8) == score["RandomForestRegressor_r2_difference"]
     else:
-        assert pytest.approx(-0.05648909197410379, abs=1e-8) == score["RandomForestRegressor_r2_difference"]
+        assert pytest.approx(-0.05648892075668577, abs=1e-8) == score["RandomForestRegressor_r2_difference"]
 
     unset_all_random_seeds()
 
diff --git a/tests/unit/evaluation/quality/test_multi_target_modeling_difference.py b/tests/unit/evaluation/quality/test_multi_target_modeling_difference.py
index 8d15b093..b2eeb73b 100644
--- a/tests/unit/evaluation/quality/test_multi_target_modeling_difference.py
+++ b/tests/unit/evaluation/quality/test_multi_target_modeling_difference.py
@@ -8,6 +8,7 @@
 from midst_toolkit.common.enumerations import ColumnType
 from midst_toolkit.common.random import set_all_random_seeds, unset_all_random_seeds
 from midst_toolkit.evaluation.quality.multi_target_modeling_difference import MultiTargetModelingDifference
+from tests.utils.architecture import is_apple_silicon
 
 
 def get_regression_data() -> tuple[pd.DataFrame, pd.DataFrame]:
@@ -189,7 +190,10 @@ def test_multi_target_modeling_difference_with_two_cat_targets() -> None:
 
     score = metric.compute(real_data, synthetic_data, holdout_data)
 
-    assert pytest.approx(-0.15399437057829385, abs=1e-8) == score["avg_f1_difference"]
+    if is_apple_silicon():
+        assert pytest.approx(-0.153918889187196, abs=1e-8) == score["avg_f1_difference"]
+    else:
+        assert pytest.approx(-0.153918889187196, abs=1e-8) == score["avg_f1_difference"]
 
     # Test that nothing breaks when we have no numerical columns
 
diff --git a/uv.lock b/uv.lock
index c7985e19..b4396ba7 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -8,16 +8,16 @@ resolution-markers = [
 
 [[package]]
 name = "alembic"
-version = "1.17.2"
+version = "1.18.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
     { name = "sqlalchemy" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064, upload-time = "2025-11-14T20:35:04.057Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/49/cc/aca263693b2ece99fa99a09b6d092acb89973eb2bb575faef1777e04f8b4/alembic-1.18.1.tar.gz", hash = "sha256:83ac6b81359596816fb3b893099841a0862f2117b2963258e965d70dc62fb866", size = 2044319, upload-time = "2026-01-14T18:53:14.907Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ba/88/6237e97e3385b57b5f1528647addea5cc03d4d65d5979ab24327d41fb00d/alembic-1.17.2-py3-none-any.whl", hash = "sha256:f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6", size = 248554, upload-time = "2025-11-14T20:35:05.699Z" },
+    { url = "https://files.pythonhosted.org/packages/83/36/cd9cb6101e81e39076b2fbe303bfa3c85ca34e55142b0324fcbf22c5c6e2/alembic-1.18.1-py3-none-any.whl", hash = "sha256:f1c3b0920b87134e851c25f1f7f236d8a332c34b75416802d06971df5d1b7810", size = 260973, upload-time = "2026-01-14T18:53:17.533Z" },
 ]
 
 [[package]]
@@ -99,30 +99,30 @@ wheels = [
 
 [[package]]
 name = "boto3"
-version = "1.42.2"
+version = "1.42.28"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "botocore" },
     { name = "jmespath" },
     { name = "s3transfer" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e4/3b/be2c0175ff58d6abef88467d7e93225231059621181bc0cfd32fd8b05260/boto3-1.42.2.tar.gz", hash = "sha256:2b403f503bfe8486fd273e41f0b5a033d0e8dad5d94c5a5c0669e92272bd4f17", size = 112828, upload-time = "2025-12-03T17:50:22.599Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/aa/a44ea8c8ee8239f3f7c32cce966512c846297df5fe48b56db6882f3b7ca0/boto3-1.42.28.tar.gz", hash = "sha256:7d56c298b8d98f5e9b04cf5d6627f68e7792e25614533aef17f815681b5e1096", size = 112846, upload-time = "2026-01-14T20:37:21.448Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/54/3f/ba8d7e362ab595279663fdc77902ee46592252ea7210596bdcc3d2eee12b/boto3-1.42.2-py3-none-any.whl", hash = "sha256:e93c55fecfecc6f05de604288d216a49b06a3c2c53421848ca4afad55f0614b7", size = 140622, upload-time = "2025-12-03T17:50:20.106Z" },
+    { url = "https://files.pythonhosted.org/packages/69/35/5d95169ed145f0c49ebfeb6a5228ab63d54e95a2c7a43f0f0eb893540660/boto3-1.42.28-py3-none-any.whl", hash = "sha256:7994bc2a094c1894f6a4221a1696c5d18af6c9c888191051866f1d05c4fba431", size = 140575, upload-time = "2026-01-14T20:37:20.098Z" },
 ]
 
 [[package]]
 name = "botocore"
-version = "1.42.2"
+version = "1.42.28"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jmespath" },
     { name = "python-dateutil" },
     { name = "urllib3" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d7/29/1c9b4f73a738d1464c07ccd8b74ed7f21d7f00319bfd6e397a0e897fd61f/botocore-1.42.2.tar.gz", hash = "sha256:3dbeba76168764219cbe392aa67cbc9265cfa05b09970ed5f2e0c786b8ac5010", size = 14843197, upload-time = "2025-12-03T17:50:11.065Z" }
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