gaitsetpy.classification.models.random_forest API documentation

# gaitsetpy.classification.models.random_forest

Random Forest Classification Model

This module contains the RandomForestModel class which inherits from BaseClassificationModel and provides Random Forest classification functionality.

Maintainer: @aharshit123456

View Source

      1'''
      2Random Forest Classification Model
      3
      4This module contains the RandomForestModel class which inherits from BaseClassificationModel
      5and provides Random Forest classification functionality.
      6
      7Maintainer: @aharshit123456
      8'''
      9
     10import joblib
     11import numpy as np
     12from typing import List, Dict, Any, Optional, Union
     13from sklearn.ensemble import RandomForestClassifier
     14from sklearn.model_selection import train_test_split
     15from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
     16from ...core.base_classes import BaseClassificationModel
     17from ..utils.preprocess import preprocess_features
     18
     19
     20class RandomForestModel(BaseClassificationModel):
     21    """
     22    Random Forest classification model.
     23
     24    This class provides Random Forest classification functionality with
     25    comprehensive training, prediction, and evaluation capabilities.
     26    """
     27
     28    def __init__(self, n_estimators: int = 100, random_state: int = 42, max_depth: Optional[int] = None):
     29        super().__init__(
     30            name="random_forest",
     31            description="Random Forest classifier for gait data classification"
     32        )
     33        self.config = {
     34            'n_estimators': n_estimators,
     35            'random_state': random_state,
     36            'max_depth': max_depth
     37        }
     38        self.model = RandomForestClassifier(
     39            n_estimators=n_estimators,
     40            random_state=random_state,
     41            max_depth=max_depth
     42        )
     43        self.feature_names = []
     44        self.class_names = []
     45
     46    def train(self, features: List[Dict], **kwargs):
     47        """
     48        Train the Random Forest model on the given features.
     49
     50        Args:
     51            features: List of feature dictionaries
     52            **kwargs: Additional arguments including test_size, validation_split
     53        """
     54        # Preprocess features
     55        X, y = preprocess_features(features)
     56
     57        # Store feature and class information
     58        self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
     59        self.class_names = list(set(y))
     60
     61        # Split data if test_size is specified
     62        test_size = kwargs.get('test_size', 0.2)
     63        validation_split = kwargs.get('validation_split', True)
     64
     65        if validation_split:
     66            X_train, X_test, y_train, y_test = train_test_split(
     67                X, y, test_size=test_size, random_state=self.config['random_state']
     68            )
     69
     70            # Train model
     71            self.model.fit(X_train, y_train)
     72
     73            # Store validation data for later evaluation
     74            self.X_test = X_test
     75            self.y_test = y_test
     76
     77            # Print training accuracy
     78            train_accuracy = self.model.score(X_train, y_train)
     79            test_accuracy = self.model.score(X_test, y_test)
     80
     81            print(f"Training accuracy: {train_accuracy:.4f}")
     82            print(f"Validation accuracy: {test_accuracy:.4f}")
     83        else:
     84            # Train on all data
     85            self.model.fit(X, y)
     86            train_accuracy = self.model.score(X, y)
     87            print(f"Training accuracy: {train_accuracy:.4f}")
     88
     89        self.trained = True
     90        print("Random Forest model trained successfully.")
     91
     92    def predict(self, features: List[Dict], **kwargs) -> Union[np.ndarray, Any]:
     93        """
     94        Make predictions using the trained Random Forest model.
     95
     96        Args:
     97            features: List of feature dictionaries
     98            **kwargs: Additional arguments including return_probabilities
     99
    100        Returns:
    101            Array of predictions or probabilities
    102        """
    103        if not self.trained:
    104            raise ValueError("Model must be trained before making predictions")
    105
    106        # Preprocess features
    107        X, _ = preprocess_features(features)
    108
    109        # Make predictions
    110        return_probabilities = kwargs.get('return_probabilities', False)
    111
    112        if return_probabilities:
    113            return self.model.predict_proba(X)
    114        else:
    115            return self.model.predict(X)
    116
    117    def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]:
    118        """
    119        Evaluate the Random Forest model performance.
    120
    121        Args:
    122            features: List of feature dictionaries
    123            **kwargs: Additional arguments including detailed_report
    124
    125        Returns:
    126            Dictionary containing evaluation metrics
    127        """
    128        if not self.trained:
    129            raise ValueError("Model must be trained before evaluation")
    130
    131        # Use validation data if available, otherwise use provided features
    132        if hasattr(self, 'X_test') and hasattr(self, 'y_test'):
    133            X_test, y_test = self.X_test, self.y_test
    134        else:
    135            X_test, y_test = preprocess_features(features)
    136
    137        # Make predictions
    138        y_pred = self.model.predict(X_test)
    139
    140        # Calculate metrics
    141        accuracy = accuracy_score(y_test, y_pred)
    142        conf_matrix = confusion_matrix(y_test, y_pred)
    143
    144        # Basic metrics
    145        metrics = {
    146            'accuracy': accuracy,
    147            'confusion_matrix': conf_matrix.tolist()
    148        }
    149
    150        # Detailed report if requested
    151        detailed_report = kwargs.get('detailed_report', False)
    152        if detailed_report:
    153            class_report = classification_report(y_test, y_pred, output_dict=True)
    154            metrics['classification_report'] = class_report
    155
    156            # Feature importance
    157            if hasattr(self.model, 'feature_importances_'):
    158                feature_importance = dict(zip(self.feature_names, self.model.feature_importances_))
    159                metrics['feature_importance'] = feature_importance
    160
    161        return metrics
    162
    163    def save_model(self, filepath: str):
    164        """
    165        Save the trained Random Forest model to a file.
    166
    167        Args:
    168            filepath: Path to save the model
    169        """
    170        if not self.trained:
    171            raise ValueError("Model must be trained before saving")
    172
    173        # Save model with additional metadata
    174        model_data = {
    175            'model': self.model,
    176            'config': self.config,
    177            'feature_names': self.feature_names,
    178            'class_names': self.class_names,
    179            'trained': self.trained
    180        }
    181
    182        joblib.dump(model_data, filepath)
    183        print(f"Random Forest model saved to {filepath}")
    184
    185    def load_model(self, filepath: str):
    186        """
    187        Load a trained Random Forest model from a file.
    188
    189        Args:
    190            filepath: Path to the saved model
    191        """
    192        try:
    193            model_data = joblib.load(filepath)
    194
    195            # Handle legacy model format
    196            if isinstance(model_data, dict):
    197                self.model = model_data['model']
    198                self.config = model_data.get('config', self.config)
    199                self.feature_names = model_data.get('feature_names', [])
    200                self.class_names = model_data.get('class_names', [])
    201                self.trained = model_data.get('trained', True)
    202            else:
    203                # Legacy format - just the model
    204                self.model = model_data
    205                self.trained = True
    206
    207            print(f"Random Forest model loaded from {filepath}")
    208        except Exception as e:
    209            print(f"Error loading model: {e}")
    210            raise
    211
    212    def get_feature_importance(self) -> Dict[str, float]:
    213        """
    214        Get feature importance scores.
    215
    216        Returns:
    217            Dictionary mapping feature names to importance scores
    218        """
    219        if not self.trained:
    220            raise ValueError("Model must be trained to get feature importance")
    221
    222        if hasattr(self.model, 'feature_importances_'):
    223            return dict(zip(self.feature_names, self.model.feature_importances_))
    224        else:
    225            return {}
    226
    227    def predict_single(self, single_features: Dict) -> int:
    228        """
    229        Make prediction for a single feature vector.
    230
    231        Args:
    232            single_features: Dictionary containing features for a single sample
    233
    234        Returns:
    235            Predicted class
    236        """
    237        if not self.trained:
    238            raise ValueError("Model must be trained before making predictions")
    239
    240        # Convert single feature dict to format expected by preprocess_features
    241        features_list = [single_features]
    242        X, _ = preprocess_features(features_list)
    243
    244        return self.model.predict(X)[0]
    245
    246
    247# Legacy function wrapper for backward compatibility
    248def create_random_forest_model(n_estimators=100, random_state=42, max_depth=None):
    249    """
    250    Create a Random Forest model with specified parameters.
    251
    252    Args:
    253        n_estimators: Number of trees in the forest
    254        random_state: Random state for reproducibility
    255        max_depth: Maximum depth of the tree
    256
    257    Returns:
    258        RandomForestModel instance
    259    """
    260    return RandomForestModel(n_estimators=n_estimators, random_state=random_state, max_depth=max_depth)

class RandomForestModel(gaitsetpy.core.base_classes.BaseClassificationModel): View Source

     21class RandomForestModel(BaseClassificationModel):
     22    """
     23    Random Forest classification model.
     24
     25    This class provides Random Forest classification functionality with
     26    comprehensive training, prediction, and evaluation capabilities.
     27    """
     28
     29    def __init__(self, n_estimators: int = 100, random_state: int = 42, max_depth: Optional[int] = None):
     30        super().__init__(
     31            name="random_forest",
     32            description="Random Forest classifier for gait data classification"
     33        )
     34        self.config = {
     35            'n_estimators': n_estimators,
     36            'random_state': random_state,
     37            'max_depth': max_depth
     38        }
     39        self.model = RandomForestClassifier(
     40            n_estimators=n_estimators,
     41            random_state=random_state,
     42            max_depth=max_depth
     43        )
     44        self.feature_names = []
     45        self.class_names = []
     46
     47    def train(self, features: List[Dict], **kwargs):
     48        """
     49        Train the Random Forest model on the given features.
     50
     51        Args:
     52            features: List of feature dictionaries
     53            **kwargs: Additional arguments including test_size, validation_split
     54        """
     55        # Preprocess features
     56        X, y = preprocess_features(features)
     57
     58        # Store feature and class information
     59        self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
     60        self.class_names = list(set(y))
     61
     62        # Split data if test_size is specified
     63        test_size = kwargs.get('test_size', 0.2)
     64        validation_split = kwargs.get('validation_split', True)
     65
     66        if validation_split:
     67            X_train, X_test, y_train, y_test = train_test_split(
     68                X, y, test_size=test_size, random_state=self.config['random_state']
     69            )
     70
     71            # Train model
     72            self.model.fit(X_train, y_train)
     73
     74            # Store validation data for later evaluation
     75            self.X_test = X_test
     76            self.y_test = y_test
     77
     78            # Print training accuracy
     79            train_accuracy = self.model.score(X_train, y_train)
     80            test_accuracy = self.model.score(X_test, y_test)
     81
     82            print(f"Training accuracy: {train_accuracy:.4f}")
     83            print(f"Validation accuracy: {test_accuracy:.4f}")
     84        else:
     85            # Train on all data
     86            self.model.fit(X, y)
     87            train_accuracy = self.model.score(X, y)
     88            print(f"Training accuracy: {train_accuracy:.4f}")
     89
     90        self.trained = True
     91        print("Random Forest model trained successfully.")
     92
     93    def predict(self, features: List[Dict], **kwargs) -> Union[np.ndarray, Any]:
     94        """
     95        Make predictions using the trained Random Forest model.
     96
     97        Args:
     98            features: List of feature dictionaries
     99            **kwargs: Additional arguments including return_probabilities
    100
    101        Returns:
    102            Array of predictions or probabilities
    103        """
    104        if not self.trained:
    105            raise ValueError("Model must be trained before making predictions")
    106
    107        # Preprocess features
    108        X, _ = preprocess_features(features)
    109
    110        # Make predictions
    111        return_probabilities = kwargs.get('return_probabilities', False)
    112
    113        if return_probabilities:
    114            return self.model.predict_proba(X)
    115        else:
    116            return self.model.predict(X)
    117
    118    def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]:
    119        """
    120        Evaluate the Random Forest model performance.
    121
    122        Args:
    123            features: List of feature dictionaries
    124            **kwargs: Additional arguments including detailed_report
    125
    126        Returns:
    127            Dictionary containing evaluation metrics
    128        """
    129        if not self.trained:
    130            raise ValueError("Model must be trained before evaluation")
    131
    132        # Use validation data if available, otherwise use provided features
    133        if hasattr(self, 'X_test') and hasattr(self, 'y_test'):
    134            X_test, y_test = self.X_test, self.y_test
    135        else:
    136            X_test, y_test = preprocess_features(features)
    137
    138        # Make predictions
    139        y_pred = self.model.predict(X_test)
    140
    141        # Calculate metrics
    142        accuracy = accuracy_score(y_test, y_pred)
    143        conf_matrix = confusion_matrix(y_test, y_pred)
    144
    145        # Basic metrics
    146        metrics = {
    147            'accuracy': accuracy,
    148            'confusion_matrix': conf_matrix.tolist()
    149        }
    150
    151        # Detailed report if requested
    152        detailed_report = kwargs.get('detailed_report', False)
    153        if detailed_report:
    154            class_report = classification_report(y_test, y_pred, output_dict=True)
    155            metrics['classification_report'] = class_report
    156
    157            # Feature importance
    158            if hasattr(self.model, 'feature_importances_'):
    159                feature_importance = dict(zip(self.feature_names, self.model.feature_importances_))
    160                metrics['feature_importance'] = feature_importance
    161
    162        return metrics
    163
    164    def save_model(self, filepath: str):
    165        """
    166        Save the trained Random Forest model to a file.
    167
    168        Args:
    169            filepath: Path to save the model
    170        """
    171        if not self.trained:
    172            raise ValueError("Model must be trained before saving")
    173
    174        # Save model with additional metadata
    175        model_data = {
    176            'model': self.model,
    177            'config': self.config,
    178            'feature_names': self.feature_names,
    179            'class_names': self.class_names,
    180            'trained': self.trained
    181        }
    182
    183        joblib.dump(model_data, filepath)
    184        print(f"Random Forest model saved to {filepath}")
    185
    186    def load_model(self, filepath: str):
    187        """
    188        Load a trained Random Forest model from a file.
    189
    190        Args:
    191            filepath: Path to the saved model
    192        """
    193        try:
    194            model_data = joblib.load(filepath)
    195
    196            # Handle legacy model format
    197            if isinstance(model_data, dict):
    198                self.model = model_data['model']
    199                self.config = model_data.get('config', self.config)
    200                self.feature_names = model_data.get('feature_names', [])
    201                self.class_names = model_data.get('class_names', [])
    202                self.trained = model_data.get('trained', True)
    203            else:
    204                # Legacy format - just the model
    205                self.model = model_data
    206                self.trained = True
    207
    208            print(f"Random Forest model loaded from {filepath}")
    209        except Exception as e:
    210            print(f"Error loading model: {e}")
    211            raise
    212
    213    def get_feature_importance(self) -> Dict[str, float]:
    214        """
    215        Get feature importance scores.
    216
    217        Returns:
    218            Dictionary mapping feature names to importance scores
    219        """
    220        if not self.trained:
    221            raise ValueError("Model must be trained to get feature importance")
    222
    223        if hasattr(self.model, 'feature_importances_'):
    224            return dict(zip(self.feature_names, self.model.feature_importances_))
    225        else:
    226            return {}
    227
    228    def predict_single(self, single_features: Dict) -> int:
    229        """
    230        Make prediction for a single feature vector.
    231
    232        Args:
    233            single_features: Dictionary containing features for a single sample
    234
    235        Returns:
    236            Predicted class
    237        """
    238        if not self.trained:
    239            raise ValueError("Model must be trained before making predictions")
    240
    241        # Convert single feature dict to format expected by preprocess_features
    242        features_list = [single_features]
    243        X, _ = preprocess_features(features_list)
    244
    245        return self.model.predict(X)[0]

Random Forest classification model.

This class provides Random Forest classification functionality with comprehensive training, prediction, and evaluation capabilities.

RandomForestModel( n_estimators: int = 100, random_state: int = 42, max_depth: Optional[int] = None) View Source

    29    def __init__(self, n_estimators: int = 100, random_state: int = 42, max_depth: Optional[int] = None):
    30        super().__init__(
    31            name="random_forest",
    32            description="Random Forest classifier for gait data classification"
    33        )
    34        self.config = {
    35            'n_estimators': n_estimators,
    36            'random_state': random_state,
    37            'max_depth': max_depth
    38        }
    39        self.model = RandomForestClassifier(
    40            n_estimators=n_estimators,
    41            random_state=random_state,
    42            max_depth=max_depth
    43        )
    44        self.feature_names = []
    45        self.class_names = []

Initialize the classification model.

Args: name: Name of the classification model description: Description of the classification model

config
model
feature_names
class_names
def train(self, features: List[Dict], **kwargs): View Source

    47    def train(self, features: List[Dict], **kwargs):
    48        """
    49        Train the Random Forest model on the given features.
    50
    51        Args:
    52            features: List of feature dictionaries
    53            **kwargs: Additional arguments including test_size, validation_split
    54        """
    55        # Preprocess features
    56        X, y = preprocess_features(features)
    57
    58        # Store feature and class information
    59        self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
    60        self.class_names = list(set(y))
    61
    62        # Split data if test_size is specified
    63        test_size = kwargs.get('test_size', 0.2)
    64        validation_split = kwargs.get('validation_split', True)
    65
    66        if validation_split:
    67            X_train, X_test, y_train, y_test = train_test_split(
    68                X, y, test_size=test_size, random_state=self.config['random_state']
    69            )
    70
    71            # Train model
    72            self.model.fit(X_train, y_train)
    73
    74            # Store validation data for later evaluation
    75            self.X_test = X_test
    76            self.y_test = y_test
    77
    78            # Print training accuracy
    79            train_accuracy = self.model.score(X_train, y_train)
    80            test_accuracy = self.model.score(X_test, y_test)
    81
    82            print(f"Training accuracy: {train_accuracy:.4f}")
    83            print(f"Validation accuracy: {test_accuracy:.4f}")
    84        else:
    85            # Train on all data
    86            self.model.fit(X, y)
    87            train_accuracy = self.model.score(X, y)
    88            print(f"Training accuracy: {train_accuracy:.4f}")
    89
    90        self.trained = True
    91        print("Random Forest model trained successfully.")

Train the Random Forest model on the given features.

Args: features: List of feature dictionaries **kwargs: Additional arguments including test_size, validation_split

def predict(self, features: List[Dict], **kwargs) -> Union[numpy.ndarray, Any]: View Source

     93    def predict(self, features: List[Dict], **kwargs) -> Union[np.ndarray, Any]:
     94        """
     95        Make predictions using the trained Random Forest model.
     96
     97        Args:
     98            features: List of feature dictionaries
     99            **kwargs: Additional arguments including return_probabilities
    100
    101        Returns:
    102            Array of predictions or probabilities
    103        """
    104        if not self.trained:
    105            raise ValueError("Model must be trained before making predictions")
    106
    107        # Preprocess features
    108        X, _ = preprocess_features(features)
    109
    110        # Make predictions
    111        return_probabilities = kwargs.get('return_probabilities', False)
    112
    113        if return_probabilities:
    114            return self.model.predict_proba(X)
    115        else:
    116            return self.model.predict(X)

Make predictions using the trained Random Forest model.

Args: features: List of feature dictionaries **kwargs: Additional arguments including return_probabilities

Returns: Array of predictions or probabilities

def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]: View Source

    118    def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]:
    119        """
    120        Evaluate the Random Forest model performance.
    121
    122        Args:
    123            features: List of feature dictionaries
    124            **kwargs: Additional arguments including detailed_report
    125
    126        Returns:
    127            Dictionary containing evaluation metrics
    128        """
    129        if not self.trained:
    130            raise ValueError("Model must be trained before evaluation")
    131
    132        # Use validation data if available, otherwise use provided features
    133        if hasattr(self, 'X_test') and hasattr(self, 'y_test'):
    134            X_test, y_test = self.X_test, self.y_test
    135        else:
    136            X_test, y_test = preprocess_features(features)
    137
    138        # Make predictions
    139        y_pred = self.model.predict(X_test)
    140
    141        # Calculate metrics
    142        accuracy = accuracy_score(y_test, y_pred)
    143        conf_matrix = confusion_matrix(y_test, y_pred)
    144
    145        # Basic metrics
    146        metrics = {
    147            'accuracy': accuracy,
    148            'confusion_matrix': conf_matrix.tolist()
    149        }
    150
    151        # Detailed report if requested
    152        detailed_report = kwargs.get('detailed_report', False)
    153        if detailed_report:
    154            class_report = classification_report(y_test, y_pred, output_dict=True)
    155            metrics['classification_report'] = class_report
    156
    157            # Feature importance
    158            if hasattr(self.model, 'feature_importances_'):
    159                feature_importance = dict(zip(self.feature_names, self.model.feature_importances_))
    160                metrics['feature_importance'] = feature_importance
    161
    162        return metrics

Evaluate the Random Forest model performance.

Args: features: List of feature dictionaries **kwargs: Additional arguments including detailed_report

Returns: Dictionary containing evaluation metrics

def save_model(self, filepath: str): View Source

    164    def save_model(self, filepath: str):
    165        """
    166        Save the trained Random Forest model to a file.
    167
    168        Args:
    169            filepath: Path to save the model
    170        """
    171        if not self.trained:
    172            raise ValueError("Model must be trained before saving")
    173
    174        # Save model with additional metadata
    175        model_data = {
    176            'model': self.model,
    177            'config': self.config,
    178            'feature_names': self.feature_names,
    179            'class_names': self.class_names,
    180            'trained': self.trained
    181        }
    182
    183        joblib.dump(model_data, filepath)
    184        print(f"Random Forest model saved to {filepath}")

Save the trained Random Forest model to a file.

Args: filepath: Path to save the model

def load_model(self, filepath: str): View Source

    186    def load_model(self, filepath: str):
    187        """
    188        Load a trained Random Forest model from a file.
    189
    190        Args:
    191            filepath: Path to the saved model
    192        """
    193        try:
    194            model_data = joblib.load(filepath)
    195
    196            # Handle legacy model format
    197            if isinstance(model_data, dict):
    198                self.model = model_data['model']
    199                self.config = model_data.get('config', self.config)
    200                self.feature_names = model_data.get('feature_names', [])
    201                self.class_names = model_data.get('class_names', [])
    202                self.trained = model_data.get('trained', True)
    203            else:
    204                # Legacy format - just the model
    205                self.model = model_data
    206                self.trained = True
    207
    208            print(f"Random Forest model loaded from {filepath}")
    209        except Exception as e:
    210            print(f"Error loading model: {e}")
    211            raise

Load a trained Random Forest model from a file.

Args: filepath: Path to the saved model

def get_feature_importance(self) -> Dict[str, float]: View Source

    213    def get_feature_importance(self) -> Dict[str, float]:
    214        """
    215        Get feature importance scores.
    216
    217        Returns:
    218            Dictionary mapping feature names to importance scores
    219        """
    220        if not self.trained:
    221            raise ValueError("Model must be trained to get feature importance")
    222
    223        if hasattr(self.model, 'feature_importances_'):
    224            return dict(zip(self.feature_names, self.model.feature_importances_))
    225        else:
    226            return {}

Get feature importance scores.

Returns: Dictionary mapping feature names to importance scores

def predict_single(self, single_features: Dict) -> int: View Source

    228    def predict_single(self, single_features: Dict) -> int:
    229        """
    230        Make prediction for a single feature vector.
    231
    232        Args:
    233            single_features: Dictionary containing features for a single sample
    234
    235        Returns:
    236            Predicted class
    237        """
    238        if not self.trained:
    239            raise ValueError("Model must be trained before making predictions")
    240
    241        # Convert single feature dict to format expected by preprocess_features
    242        features_list = [single_features]
    243        X, _ = preprocess_features(features_list)
    244
    245        return self.model.predict(X)[0]

Make prediction for a single feature vector.

Args: single_features: Dictionary containing features for a single sample

Returns: Predicted class

##### Inherited Members

gaitsetpy.core.base_classes.BaseClassificationModel
  name
  description
  trained
  configure
  get_info

def create_random_forest_model(n_estimators=100, random_state=42, max_depth=None): View Source

    249def create_random_forest_model(n_estimators=100, random_state=42, max_depth=None):
    250    """
    251    Create a Random Forest model with specified parameters.
    252
    253    Args:
    254        n_estimators: Number of trees in the forest
    255        random_state: Random state for reproducibility
    256        max_depth: Maximum depth of the tree
    257
    258    Returns:
    259        RandomForestModel instance
    260    """
    261    return RandomForestModel(n_estimators=n_estimators, random_state=random_state, max_depth=max_depth)

Create a Random Forest model with specified parameters.

Args: n_estimators: Number of trees in the forest random_state: Random state for reproducibility max_depth: Maximum depth of the tree

Returns: RandomForestModel instance