Target Extraction

The target extraction process is handled by the extract_targets method, which prepares the target variables for training.

---

Target Type Identification

The method identifies each target as either numeric or categorical.

---

Ethereum Address Handling

Ethereum addresses are treated as categorical variables.

---

Numeric Target Scaling

For numeric targets:

scaler = StandardScaler()
y_scaled = scaler.fit_transform(y_values)

---

Categorical Target Encoding

For categorical targets:

encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')
one_hot_values = encoder.fit_transform(np.array(string_values).reshape(-1, 1))

---

Multi-target Handling

The method can handle multiple targets, combining them into a single 2D numpy array.

---

Usage

Both methods are called during the model training process:

X, feature_types, feature_encoders, n_features = self.feature_engineering(data, features, mode='train')
y, target_types, target_encoders = self.extract_targets(data, targets)

---

Advantages

• Handles various data types automatically

• Applies appropriate transformations for each data type

• Supports multi-target scenarios

• Preserves information about feature and target transformations for consistent prediction